Skip to main content
SpringerLink
Log in

Deep learning-assisted medical image compression challenges and opportunities: systematic review

  • Review
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Over the preceding decade, there has been a discernible surge in the prominence of artificial intelligence, marked by the development of various methodologies, among which deep learning emerges as a particularly auspicious technique. The captivating attribute of deep learning, characterised by its capacity to glean intricate feature representations from data, has served as a catalyst for pioneering approaches and methodologies spanning a multitude of domains. In the face of the burgeoning exponential growth in digital medical image data, the exigency for adept image compression methodologies has become increasingly pronounced. These methodologies are designed to preserve bandwidth and storage resources, thereby ensuring the seamless and efficient transmission of data within medical applications. The critical nature of medical image compression accentuates the imperative to confront the challenges precipitated by the escalating deluge of medical image data. This review paper undertakes a comprehensive examination of medical image compression, with a predominant focus on sophisticated, research-driven deep learning techniques. It delves into a spectrum of approaches, encompassing the amalgamation of deep learning with conventional compression algorithms and the application of deep learning to enhance compression quality. Additionally, the review endeavours to explicate these fundamental concepts, elucidating their inherent characteristics, merits, and limitations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Data availability

The data used in this review paper were sourced from publicly available academic databases, including PubMed, Web of Science, and Google Scholar. The search strategy involved a systematic review of relevant literature, elaborated extensively in Sect. 3. All data sources are freely accessible online. In cases where certain data are not publicly available, interested researchers may request access by contacting the respective authors or institutions.

References

  1. Ackerman MJ (1998) The visible human project. Proc IEEE 86(3):504–511. https://doi.org/10.1109/5.662875

    Article  Google Scholar 

  2. Abadi E, Segars WP, Tsui BMW et al (2020) Virtual clinical trials in medical imaging: a review. J Med Imaging 7(4):042805. https://doi.org/10.1117/1.JMI.7.4.042805

    Article  Google Scholar 

  3. Aggarwal R, Sounderajah V, Martin G et al (2021) Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis. NPJ Dig Med 4(1):65. https://doi.org/10.1038/s41746-021-00438-z

    Article  Google Scholar 

  4. Agustsson E, Tschannen M, Mentzer F, et al (2019) Generative adversarial networks for extreme learned image compression. In: 2019 IEEE/CVF international conference on computer vision (ICCV), pp 221–231. https://doi.org/10.1109/ICCV.2019.00031

  5. Ahirwar K (2019) Generative adversarial networks projects: build next-generation generative models using TensorFlow and Keras. Packt Publishing Ltd

  6. Ahmad HF, Rafique W, Rasool RU et al (2023) Leveraging 6g, extended reality, and iot big data analytics for healthcare: a review. Comput Sci Rev 48(100):558. https://doi.org/10.1016/j.cosrev.2023.100558

    Article  Google Scholar 

  7. Albertina B, Watson M, Holback C et al (2016) Radiology data from the cancer genome atlas lung adenocarcinoma [TCGA-LUAD] collection. Cancer Imaging Arch 10:K9

    Google Scholar 

  8. American College of Radiology and others. MedPix™: medical image database. https://medpix.nlm.nih.gov/home

  9. Anwar SM, Majid M, Qayyum A et al (2018) Medical image analysis using convolutional neural networks: a review. J Med Syst 42(11):226. https://doi.org/10.1007/s10916-018-1088-1

    Article  Google Scholar 

  10. Arunava D (2018) Malaria cell images dataset. https://www.kaggle.com/iarunava/cell-images-for-detecting-malaria

  11. Avrin D, Morin R, Piraino D et al (2006) Storage, transmission, and retrieval of digital mammography, including recommendations on image compression. J Am College Radiol 3(8):609–614. https://doi.org/10.1016/j.jacr.2006.03.006. Special Issue: Digital Mammography

  12. Ayoobkhan MUA, Chikkannan E, Ramakrishnan K (2018) Feed-forward neural network-based predictive image coding for medical image compression. Arab J Sci Eng 43(8):4239–4247. https://doi.org/10.1007/s13369-017-2837-z

    Article  Google Scholar 

  13. Aziz SA, Sam SM, Mohamed N et al (2020) The comprehensive review of neural network: an intelligent medical image compression for data sharing. Int J Integr Eng 12(7):81–89

    Google Scholar 

  14. Bairagi VK, Sapkal AM (2013) ROI-based DICOM image compression for telemedicine. Sadhana 38(1):123–131. https://doi.org/10.1007/s12046-013-0126-4

    Article  Google Scholar 

  15. Bakas S, Akbari H, Sotiras A et al (2017) Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci Data 4(170):117. https://doi.org/10.1038/sdata.2017.117

    Article  Google Scholar 

  16. Bakas S, Reyes M, Jakab A, et al (2019) Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge. arXiv:1811.02629

  17. Bakator M, Radosav D (2018) Deep learning and medical diagnosis: a review of literature. Multimodal Technol Inter. https://doi.org/10.3390/mti2030047

    Article  Google Scholar 

  18. Ballé J, Laparra V, Simoncelli EP (2017) End-to-end optimized image compression. In: 5th international conference on learning representations, ICLR 2017, Toulon, France, April 24–26, 2017, conference track proceedings. OpenReview.net

  19. Ballé J, Minnen D, Singh S, et al (2018) Variational image compression with a scale hyperprior. In: 6th int. conf. on learning representations (ICLR)

  20. Bellard F (2014) BPG image format. http://bellard.org/bpg

  21. Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798–1828. https://doi.org/10.1109/TPAMI.2013.50

    Article  Google Scholar 

  22. Bertine H, Faynberg I, Lu HL (2004) Overview of data and telecommunications security standardization efforts in ISO, IEC, ITU, and IETF. Bell Labs Tech J 8(4):203–229. https://doi.org/10.1002/bltj.10096

    Article  Google Scholar 

  23. Beutel J, Kundel HL, Kim Y et al (2000) Handbook of medical imaging: display and PACS, vol 3. SPIE Press

  24. Bewick V, Cheek L, Ball J (2004) Statistics review 13: receiver operating characteristic curves. Crit Care 8(6):508. https://doi.org/10.1186/cc3000

    Article  Google Scholar 

  25. Beyer T, Bidaut L, Dickson J et al (2020) What scans we will read: imaging instrumentation trends in clinical oncology. Cancer Imaging 20(1):38. https://doi.org/10.1186/s40644-020-00312-3

    Article  Google Scholar 

  26. Bhatt D, Patel C, Talsania H et al (2021) Cnn variants for computer vision: history, architecture, application, challenges and future scope. Electronics. https://doi.org/10.3390/electronics10202470

    Article  Google Scholar 

  27. Bhattacharya S, Somayaji SRK, Gadekallu TR et al (2022) A review on deep learning for future smart cities. Internet Technol Lett 5(1):e187. https://doi.org/10.1002/itl2.187

    Article  Google Scholar 

  28. Bien N, Rajpurkar P, Ball RL et al (2018) Deep-learning-assisted diagnosis for knee magnetic resonance imaging: development and retrospective validation of mrnet. PLoS Med 15(11):1–19. https://doi.org/10.1371/journal.pmed.1002699

    Article  Google Scholar 

  29. Bindu PV, Afthab J (2021) Region of interest based medical image compression using DCT and capsule autoencoder for telemedicine applications. In: 2021 4th international conference on electrical, computer and communication technologies (ICECCT), pp 1–7. https://doi.org/10.1109/ICECCT52121.2021.9616748

  30. Bishop CM (2006) Pattern recognition and machine learning, Information science and statistics, 1st edn. Springer, New York. https://doi.org/10.1007/978-0-387-31073-2. https://www.springer.com/gp/book/9780387310732

  31. Boliek M, Christopoulos C, Majani E (2000) Information technology: JPEG2000 image coding system. ISO/IEC JTC1/SC29 WG1, JPEG 2000, April 2000, Final Committee Draft Version 1.0, ISO/IEC JTC1/SC29/WG1 N1643. Available at: JPEG2000. https://doi.org/10.3403/bsisoiec15444

  32. Boopathiraja S, Punitha V, Kalavathi P et al (2022) Computational 2D and 3D medical image data compression models. Arch Comput Methods Eng 29(2):975–1007. https://doi.org/10.1007/s11831-021-09602-w

    Article  Google Scholar 

  33. Bossard L, Guillaumin M, Van Gool L (2014) Food-101—mining discriminative components with random forests. In: Fleet D, Pajdla T, Schiele B et al (eds) Computer vision—ECCV 2014. Springer, Cham, pp 446–461

    Chapter  Google Scholar 

  34. Boukhamla A, Merouani HF, Sissaoui H (2016) Parallelization of filtered back-projection algorithm for computed tomography. Evolv Syst 7(3):197–205. https://doi.org/10.1007/s12530-015-9139-z

    Article  Google Scholar 

  35. Brownlee J (2019) Generative adversarial networks with python: deep learning generative models for image synthesis and image translation. Machine Learning Mastery, San Francisco, CA, USA

    Google Scholar 

  36. Bruylants T, Munteanu A, Schelkens P (2015) Wavelet based volumetric medical image compression. Signal Process: Image Commun 31:112–133. https://doi.org/10.1016/j.image.2014.12.007

    Article  Google Scholar 

  37. Büchel C, Friston KJ (1997) Modulation of connectivity in visual pathways by attention: cortical interactions evaluated with structural equation modelling and fMRI. Cereb Cortex 7(8):768–778. https://doi.org/10.1093/cercor/7.8.768. https://academic.oup.com/cercor/article-pdf/7/8/768/9752615/070768.pdf

  38. Cai L, Gao J, Zhao D (2020) A review of the application of deep learning in medical image classification and segmentation. Ann Transl Med 8(11):713. https://doi.org/10.21037/atm.2020.02.44

  39. Calderbank A, Daubechies I, Sweldens W et al (1998) Wavelet transforms that map integers to integers. Appl Comput Harmon Anal 5(3):332–369. https://doi.org/10.1006/acha.1997.0238

    Article  MathSciNet  Google Scholar 

  40. Cardenas C, Mohamed A, Sharp G et al (2019). Data from aapm rt-mac grand challenge. https://doi.org/10.7937/tcia.2019.bcfjqfqb

  41. Carrato S, et al (1992) Neural networks for image compression. In: Neural networks: advances and applications, vol 2. North-Holland, pp 177–198

  42. Carrizales-Espinoza D, Sanchez-Gallegos DD, Gonzalez-Compean JL et al (2023) FedFlow: a federated platform to build secure sharing and synchronization services for health dataflows. Computing 105(5):1019–1037. https://doi.org/10.1007/s00607-021-01044-3

    Article  Google Scholar 

  43. Cavaro-Ménard C, Naït-Ali A, Tanguy JY et al (2008) Specificities of physiological signals and medical images, Chap 3. Wiley, pp 43–76. https://doi.org/10.1002/9780470611159.ch3

  44. Celard P, Iglesias EL, Sorribes-Fdez JM et al (2023) A survey on deep learning applied to medical images: from simple artificial neural networks to generative models. Neural Comput Appl 35(3):2291–2323. https://doi.org/10.1007/s00521-022-07953-4

    Article  Google Scholar 

  45. Cereda CW, Christensen S, Campbell BC et al (2016) A benchmarking tool to evaluate computer tomography perfusion infarct core predictions against a DWI standard. J Cereb Blood Flow Metabol 36(10):1780–1789

    Article  Google Scholar 

  46. Chakraborty DP (2013) A brief history of free-response receiver operating characteristic paradigm data analysis. Acad Radiol 20(7):915–919. https://doi.org/10.1016/j.acra.2013.03.001

    Article  Google Scholar 

  47. Chang CI (2021) An effective evaluation tool for hyperspectral target detection: 3d receiver operating characteristic curve analysis. IEEE Trans Geosci Remote Sens 59(6):5131–5153. https://doi.org/10.1109/TGRS.2020.3021671

    Article  Google Scholar 

  48. Chang Y, Yan L, Wu T et al (2016) Remote sensing image stripe noise removal: From image decomposition perspective. IEEE Trans Geosci Remote Sens 54(12):7018–7031. https://doi.org/10.1109/TGRS.2016.2594080

    Article  Google Scholar 

  49. Chebli A, Djebbar A, Merouani-Djellali HF (2020) Improving the performance of computer-aided diagnosis systems using semi-supervised learning: a survey and analysis. Int J Intell Inf Database Syst 13(2/3/4):454–478. https://doi.org/10.1504/IJIIDS.2020.109466

    Article  Google Scholar 

  50. Cho ZH, Jones JP, Singh M (1993) Foundations of medical imaging. Wiley, New York

    Google Scholar 

  51. Choudhary AK, Harding JA, Tiwari MK (2009) Data mining in manufacturing: a review based on the kind of knowledge. J Intell Manuf 20(5):501–521. https://doi.org/10.1007/s10845-008-0145-x

    Article  Google Scholar 

  52. Christin S, Hervet E, Lecomte N (2019) Applications for deep learning in ecology. Methods Ecol Evolut 10(10):1632–1644. https://doi.org/10.1111/2041-210X.13256

    Article  Google Scholar 

  53. Christopoulos C, Skodras A, Ebrahimi T (2000) The jpeg2000 still image coding system: an overview. IEEE Trans Consum Electron 46(4):1103–1127. https://doi.org/10.1109/30.920468

    Article  Google Scholar 

  54. Clark K, Vendt B, Smith K et al (2013) The cancer imaging archive (TCIA): maintaining and operating a public information repository. J Digit Imaging 26(6):1045–1057. https://doi.org/10.1007/s10278-013-9622-7

    Article  Google Scholar 

  55. Competition K (2017) Intel & MobileODT cervical cancer screening. https://www.kaggle.com/competition

  56. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297. https://doi.org/10.1007/BF00994018

    Article  Google Scholar 

  57. Cosman P, Tseng C, Gray R et al (1993) Tree-structured vector quantization of ct chest scans: image quality and diagnostic accuracy. IEEE Trans Med Imaging 12(4):727–739. https://doi.org/10.1109/42.251124

    Article  Google Scholar 

  58. Cosman P, Gray R, Olshen R (1994) Evaluating quality of compressed medical images: SNR, subjective rating, and diagnostic accuracy. Proc IEEE 82(6):919–932. https://doi.org/10.1109/5.286196

    Article  Google Scholar 

  59. Cosman P, Gray R, Olshen R (2000) Quality evaluation for compressed medical images: fundamentals. Academic Press, USA, pp 803–819

    Google Scholar 

  60. Cramer C (1998) Neural networks for image and video compression: a review. Eur J Oper Res 108(2):266–282. https://doi.org/10.1016/S0377-2217(97)00370-6

    Article  Google Scholar 

  61. Creswell A, White T, Dumoulin V et al (2018) Generative adversarial networks: an overview. IEEE Signal Process Mag 35(1):53–65. https://doi.org/10.1109/MSP.2017.2765202

    Article  Google Scholar 

  62. Daubechies I, Sweldens W (1998) Factoring wavelet transforms into lifting steps. J Four Anal Appl 4(3):247–269. https://doi.org/10.1007/BF02476026

    Article  MathSciNet  Google Scholar 

  63. Demirkaya O, Asyali MH, Sahoo PK (2008) Image processing with MATLAB: applications in medicine and biology. CRC Press

  64. Deng J, Dong W, Socher R, et al (2009a) Imagenet: a large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition, pp 248–255. https://doi.org/10.1109/CVPR.2009.5206848

  65. Deng L, Hinton G, Kingsbury B (2013) New types of deep neural network learning for speech recognition and related applications: an overview. In: 2013 IEEE international conference on acoustics, speech and signal processing, pp 8599–8603. https://doi.org/10.1109/ICASSP.2013.6639344

  66. Deng L, Yu D, Hinton G (2009b) Deep learning for speech recognition and related applications. In: NIPS workshop

  67. Dimililer K (2022) Dct-based medical image compression using machine learning. Signal, Image Video Process 16(1):55–62. https://doi.org/10.1007/s11760-021-01951-0

    Article  Google Scholar 

  68. Division of Genomic Diagnostics and Bioinformatics, Department of Pathology, UAB, Anderson Peter. Whole slide image for malaria infected red blood cells, peir-vm. https://peir-vm.path.uab.edu/index.php

  69. Dixon S (2020) Diagnostic imaging dataset statistical release. Tech Rep, NHS England

  70. Dixon S (2021) Diagnostic imaging dataset statistical release. Tech Rep, NHS England

  71. Dixon S (2022) Diagnostic imaging dataset statistical release. Tech Rep, NHS England

  72. Dong C, Loy CC, He K et al (2016) Image super-resolution using deep convolutional networks. IEEE Trans Pattern Anal Mach Intell 38(2):295–307. https://doi.org/10.1109/TPAMI.2015.2439281

    Article  Google Scholar 

  73. Dong Y, Jiang Z, Shen H et al (2017) Classification accuracies of malaria infected cells using deep convolutional neural networks based on decompressed images. SoutheastCon 2017:1–6. https://doi.org/10.1109/SECON.2017.7925268

    Article  Google Scholar 

  74. Dong Y, Pan WD, Wu D (2019) Impact of misclassification rates on compression efficiency of red blood cell images of malaria infection using deep learning. Entropy. https://doi.org/10.3390/e21111062

    Article  MathSciNet  Google Scholar 

  75. Dong C, Deng Y, Loy CC, et al (2015) Compression artifacts reduction by a deep convolutional network. In: Proceedings of the 2015 IEEE international conference on computer vision (ICCV). IEEE Computer Society, USA, ICCV ’15, pp 576–584. https://doi.org/10.1109/ICCV.2015.73

  76. Dosovitskiy A, Brox T (2016) Inverting visual representations with convolutional networks. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 4829–4837. https://doi.org/10.1109/CVPR.2016.522

  77. Dougherty G (2009) Digital image processing for medical applications. Cambridge University Press

  78. Duan L, Liao X, Xiang T (2011) A secure arithmetic coding based on Markov model. Commun Nonlinear Sci Numer Simul 16(6):2554–2562. https://doi.org/10.1016/j.cnsns.2010.09.012

    Article  MathSciNet  Google Scholar 

  79. Duszak R (2012) Medical imaging: is the growth boom over. Neiman Rep 1:1–7

    Google Scholar 

  80. Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: MHS’95. Proceedings of the 6th international symposium on micro machine and human science, pp 39–43. https://doi.org/10.1109/MHS.1995.494215

  81. Ehteshami Bejnordi B, Veta M, Johannes van Diest P et al (2017) Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 318(22):2199–2210. https://doi.org/10.1001/jama.2017.14585

    Article  Google Scholar 

  82. Elman JL (1990) Finding structure in time. Cogn Sci 14(2):179–211. https://doi.org/10.1207/s15516709cog1402_1

    Article  Google Scholar 

  83. EMC with Research & Analysis by IDC (2014) The digital universe driving data growth in healthcare. https://www.emc.com/analyst-report/digital-universe-healthcare-vertical-report-ar.pdf. Accessed 29 Dec 2015

  84. Erickson BJ (2002) Irreversible compression of medical images. J Digit Imaging 15(1):5–14. https://doi.org/10.1007/s10278-002-0001-z

    Article  Google Scholar 

  85. Felzenszwalb PF, Huttenlocher DP (2004) Efficient graph-based image segmentation. Int J Comput Vis 59(2):167–181. https://doi.org/10.1023/B:VISI.0000022288.19776.77

    Article  Google Scholar 

  86. Fukushima K (1980) Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybern 36(4):193–202. https://doi.org/10.1007/BF00344251

    Article  Google Scholar 

  87. Fukushima K, Miyake S, Ito T (1983) Neocognitron: a neural network model for a mechanism of visual pattern recognition. IEEE Trans Syst, Man, Cybern SMC 13(5):826–834. https://doi.org/10.1109/TSMC.1983.6313076

    Article  Google Scholar 

  88. Gao Z, Guo Y, Zhang J et al (2023) Hierarchical perception adversarial learning framework for compressed sensing MRI. IEEE Trans Med Imaging. https://doi.org/10.1109/TMI.2023.3240862

    Article  Google Scholar 

  89. Gao S, Xiong Z (2019) Deep enhancement for 3d HDR brain image compression. In: 2019 IEEE international conference on image processing (ICIP), pp 714–718. https://doi.org/10.1109/ICIP.2019.8803781

  90. Gao S, Zhang Y, Liu D, et al (2020) Volumetric end-to-end optimized compression for brain images. In: 2020 IEEE international conference on visual communications and image processing (VCIP), pp 503–506. https://doi.org/10.1109/VCIP49819.2020.9301767

  91. Garcia-Garcia A, Orts-Escolano S, Oprea S, et al (2017) A review on deep learning techniques applied to semantic segmentation. arXiv:1704.06857

  92. Gaudio A, Smailagic A, Faloutsos C et al (2023) Deepfixcx: explainable privacy-preserving image compression for medical image analysis. Wiley Interdisciplinary Reviews: Data Min Knowl Discov 13(4):e1495

    Google Scholar 

  93. Gia TN, Qingqing L, Queralta JP, et al (2019) Lossless compression techniques in edge computing for mission-critical applications in the iot. In: 2019 12th international conference on mobile computing and ubiquitous network (ICMU), pp 1–2. https://doi.org/10.23919/ICMU48249.2019.9006647

  94. Goldberg MA, Gazelle GS, Boland GW et al (1997) Focal hepatic lesions: effect of three-dimensional wavelet compression on detection at ct. Radiology 202(1):159–165. https://doi.org/10.1148/radiology.202.1.8988206

    Article  Google Scholar 

  95. Gómez-Brandón A, Paramá JR, Villalobos K et al (2021) Lossless compression of industrial time series with direct access. Comput Ind 132(103):503. https://doi.org/10.1016/j.compind.2021.103503

    Article  Google Scholar 

  96. Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press

  97. Goodfellow I, Pouget-Abadie J, Mirza M et al (2020) Generative adversarial networks. Commun ACM 63(11):139–144. https://doi.org/10.1145/3422622

    Article  MathSciNet  Google Scholar 

  98. Goyal M, Tatwawadi K, Chandak S, et al (2019) Deepzip: lossless data compression using recurrent neural networks. In: Bilgin A, Storer J, Marcellin M, et al (eds) Proceedings—DCC 2019. Institute of Electrical and Electronics Engineers Inc., Data Compression Conference Proceedings, p 575. https://doi.org/10.1109/DCC.2019.00087

  99. Goyal M, Tatwawadi K, Chandak S, et al (2021) Dzip: improved general-purpose loss less compression based on novel neural network modeling. In: 2021 data compression conference (DCC), pp 153–162. https://doi.org/10.1109/DCC50243.2021.00023

  100. Guo Y, Liu Y, Oerlemans A et al (2016) Deep learning for visual understanding: a review. Neurocomputing 187:27–48. https://doi.org/10.1016/j.neucom.2015.09.116

    Article  Google Scholar 

  101. Haase R, Royer LA, Steinbach P et al (2020) CLIJ: GPU-accelerated image processing for everyone. Nat Methods 17(1):5–6. https://doi.org/10.1038/s41592-019-0650-1

    Article  Google Scholar 

  102. Hakim A et al (2021) Predicting infarct core from computed tomography perfusion in acute ischemia with machine learning: lessons from the isles challenge. Stroke 52(7):2328–2337. https://doi.org/10.1161/STROKEAHA.120.030696

    Article  Google Scholar 

  103. Hamoud M, Merouani HF, Laimeche L (2015) The power laws: Zipf and inverse Zipf for automated segmentation and classification of masses within mammograms. Evolv Syst 6(3):209–227. https://doi.org/10.1007/s12530-014-9116-y

    Article  Google Scholar 

  104. Hao S, Zhou Y, Guo Y (2020) A brief survey on semantic segmentation with deep learning. Neurocomputing 406:302–321. https://doi.org/10.1016/j.neucom.2019.11.118

    Article  Google Scholar 

  105. Hazra A, Rana P, Adhikari M et al (2023) Fog computing for next-generation internet of things: fundamental, state-of-the-art and research challenges. Comput Sci Rev 48(100):549. https://doi.org/10.1016/j.cosrev.2023.100549

    Article  Google Scholar 

  106. Hendee WR, Becker GJ, Borgstede JP et al (2010) Addressing overutilization in medical imaging. Radiology 257(1):240–245. https://doi.org/10.1148/radiol.10100063. (pMID: 20736333)

    Article  Google Scholar 

  107. Hesamian MH, Jia W, He X et al (2019) Deep learning techniques for medical image segmentation: achievements and challenges. J Digit Imaging 32(4):582–596. https://doi.org/10.1007/s10278-019-00227-x

    Article  Google Scholar 

  108. Hochreiter S (2001) Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. In: Field guide to dynamical recurrent neural networks, pp 237–244. https://cir.nii.ac.jp/crid/1573668925686707968

  109. Hochreiter S (1998) The vanishing gradient problem during learning recurrent neural nets and problem solutions. Int J Uncertain, Fuzz Knowl-Based Syst 06(02):107–116. https://doi.org/10.1142/S0218488598000094

    Article  Google Scholar 

  110. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735

    Article  Google Scholar 

  111. Hu Y, Yang W, Ma Z et al (2022) Learning end-to-end lossy image compression: a benchmark. IEEE Trans Pattern Anal Mach Intell 44(8):4194–4211. https://doi.org/10.1109/TPAMI.2021.3065339

    Article  Google Scholar 

  112. Huang W, Wang W, Xu H (2006) A lossless data compression algorithm for real-time database. In: 2006 6th world congress on intelligent control and automation, pp 6645–6648. https://doi.org/10.1109/WCICA.2006.1714368

  113. Hubel DH, Wiesel TN (1959) Receptive fields of single neurones in the cat’s striate cortex. J Physiol 148(3):574–591. https://doi.org/10.1113/jphysiol.1959.sp006308

    Article  Google Scholar 

  114. Hubel DH, Wiesel TN (1959) Receptive fields of single neurones in the cat’s striate cortex. J Physiol 148(3):574

    Article  Google Scholar 

  115. Iglesias G, Talavera E, Díaz-Álvarez A (2023) A survey on gans for computer vision: recent research, analysis and taxonomy. Comput Sci Rev 48(100):553. https://doi.org/10.1016/j.cosrev.2023.100553

    Article  MathSciNet  Google Scholar 

  116. Irvin J, Rajpurkar P, Ko M et al (2019) Chexpert: a large chest radiograph dataset with uncertainty labels and expert comparison. Proc AAAI Confer Artif Intell 33(01):590–597. https://doi.org/10.1609/aaai.v33i01.3301590 (https://ojs.aaai.org/index.php/AAAI/article/view/3834)

    Article  Google Scholar 

  117. Ivakhnenko A, Lapa V (1965) Cybernetic predicting devices. CCM information corporation. In: 1st working Deep Learners with many layers, learning internal representations

  118. Jaeger S, Yu H, Antani S, et al (2018–2021) Malaria Screener, NLM—Malaria Data. https://ceb.nlm.nih.gov/repositories/malaria-datasets/

  119. Jamil S, Jalil Piran M, Kwon OJ (2023) A comprehensive survey of transformers for computer vision. Drones. https://doi.org/10.3390/drones7050287

    Article  Google Scholar 

  120. Jamil S, Piran MJ, Rahman M et al (2023) Learning-driven lossy image compression: a comprehensive survey. Eng Appl Artif Intell 123(106):361. https://doi.org/10.1016/j.engappai.2023.106361

    Article  Google Scholar 

  121. Jha GK, Thulasiraman P, Thulasiram RK (2009) PSO based neural network for time series forecasting. In: 2009 international joint conference on neural networks, pp 1422–1427. https://doi.org/10.1109/IJCNN.2009.5178707

  122. Jian B, Ma C, Sun Y et al (2023) Reconstruction of the instantaneous images distorted by surface waves via Helmholtz–Hodge decomposition. J Mar Sci Eng. https://doi.org/10.3390/jmse11010164

    Article  Google Scholar 

  123. Jiang J (1999) Image compression with neural networks—a survey. Signal Process: Image Commun 14(9):737–760. https://doi.org/10.1016/S0923-5965(98)00041-1

    Article  Google Scholar 

  124. Jiang Z, Pan WD, Shen H (2018) Universal Golomb–Rice coding parameter estimation using deep belief networks for hyperspectral image compression. IEEE J Sel Top Appl Earth Observ Remote Sens 11(10):3830–3840. https://doi.org/10.1109/JSTARS.2018.2864921

    Article  Google Scholar 

  125. Jones KN, Woode DE, Panizzi K et al (2001) PEIR digital library: online resources and authoring system. In: Proceedings of the AMIA symposium, p 1075

  126. Jordan P, Adamson PM, Bhattbhatt V et al (2022) Pediatric chest-abdomen-pelvis and abdomen-pelvis ct images with expert organ contours. Med Phys 49(5):3523–3528. https://doi.org/10.1002/mp.15485. https://aapm.onlinelibrary.wiley.com/doi/abs/10.1002/mp.15485

  127. Kamilaris A, Prenafeta-Boldú FX (2018) Deep learning in agriculture: a survey. Comput Electron Agric 147:70–90. https://doi.org/10.1016/j.compag.2018.02.016

    Article  Google Scholar 

  128. Kar A, Phani Krishna Karri S, Ghosh N, et al (2018) Fully convolutional model for variable bit length and lossy high density compression of mammograms. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) workshops

  129. Karthick G, Nithya N (2022) Healthcare informatics: emerging trends, challenges, and analysis of medical imaging, Chap 15. Wiley, pp 359–381. https://doi.org/10.1002/9781119841937.ch15

  130. Kather JN, Weis CA, Bianconi F et al (2016) Multi-class texture analysis in colorectal cancer histology. Sci Rep 6(1):27988. https://doi.org/10.1038/srep27988

    Article  Google Scholar 

  131. Kaur M, Wasson V (2015) Roi based medical image compression for telemedicine application. Procedia Comput Sci 70:579–585. https://doi.org/10.1016/j.procs.2015.10.037. Proceedings of the 4th International Conference on Eco-friendly Computing and Communication Systems

  132. Kavur AE, Selver MA, Dicle O, et al (2019) CHAOS—combined (CT-MR) healthy abdominal organ segmentation challenge data. https://doi.org/10.5281/zenodo.3362844

  133. Kavur AE, Gezer NS, Barış M et al (2021) Chaos challenge–combined (CT-MR) healthy abdominal organ segmentation. Med Image Anal 69:101950. https://doi.org/10.1016/j.media.2020.101950. https://www.sciencedirect.com/science/article/pii/S1361841520303145

  134. Khan S, Yairi T (2018) A review on the application of deep learning in system health management. Mech Syst Signal Process 107:241–265. https://doi.org/10.1016/j.ymssp.2017.11.024

    Article  Google Scholar 

  135. Kim J, Lee JK, Lee KM (2016) Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR)

  136. Kingma F, Abbeel P, Ho J (2019) Bit-swap: recursive bits-back coding for lossless compression with hierarchical latent variables. In: Chaudhuri K, Salakhutdinov R (eds) Proceedings of the 36th international conference on machine learning, proceedings of machine learning research, vol 97. PMLR, pp 3408–3417

  137. Kitchenham BA, Budgen D, Brereton P (2015) Evidence-based software engineering and systematic reviews, vol 4. CRC Press

  138. Klein U, Tu Y, Stolovitzky GA et al (2003) Transcriptional analysis of the B cell germinal center reaction. Proc Natl Acad Sci USA 100(5):2639–2644

    Article  Google Scholar 

  139. Knoll B (2020) LSTM-compress: data compression using LSTM. https://github.com/byronknoll/lstm-compress

  140. Ko JP, Chang J, Bomsztyk E et al (2005) Effect of ct image compression on computer-assisted lung nodule volume measurement. Radiology 237(1):83–88. https://doi.org/10.1148/radiol.2371041079

    Article  Google Scholar 

  141. Koff DA, Shulman H (2006) An overview of digital compression of medical images: Can we use lossy image compression in radiology? Can Assoc Radiol J 57(4):211–217

    Google Scholar 

  142. Kotera J, Wödlinger M, Keglevic M (2023) Learned lossy image compression for volumetric medical data. In: Sablatnig R, Kleber F (eds) Proceedings of the 26th computer vision winter workshop, Krems, Lower Austria, Austria. https://ceur-ws.org/Vol-3349/paper9.pdf

  143. Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60(6):84–90. https://doi.org/10.1145/3065386

    Article  Google Scholar 

  144. Lai WS, Huang JB, Ahuja N, et al (2017) Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR)

  145. Larobina M, Murino L (2014) Medical Image File Formats. J Digit Imaging 27(2):200–206. https://doi.org/10.1007/s10278-013-9657-9

    Article  Google Scholar 

  146. Larson DB, Johnson LW, Schnell BM et al (2011) National trends in ct use in the emergency department: 1995–2007. Radiology 258(1):164–173. https://doi.org/10.1148/radiol.10100640

    Article  Google Scholar 

  147. Lecun Y (1988) A theoretical framework for back-propagation. In: Touretzky D, Hinton G, Sejnowski T (eds) Proceedings of the 1988 Connectionist Models Summer School, CMU, Pittsburg. Morgan Kaufmann, pp 21–28

  148. LeCun Y, Boser B, Denker JS et al (1989) Backpropagation applied to handwritten zip code recognition. Neural Comput 1(4):541–551. https://doi.org/10.1162/neco.1989.1.4.541

    Article  Google Scholar 

  149. Lecun Y, Bottou L, Bengio Y et al (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324. https://doi.org/10.1109/5.726791

    Article  Google Scholar 

  150. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444. https://doi.org/10.1038/nature14539

    Article  Google Scholar 

  151. Ledig C, Theis L, Huszar F, et al (2017) Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR)

  152. Lee RS, Gimenez F, Hoogi A et al (2017) A curated mammography data set for use in computer-aided detection and diagnosis research. Sci Data 4(1):170177. https://doi.org/10.1038/sdata.2017.177

    Article  Google Scholar 

  153. Lee VS (2017) Annual oration: driving value through imaging. Radiology 285(1):3–11. https://doi.org/10.1148/radiol.2017170798. (PMID: 28926312)

    Article  Google Scholar 

  154. Lee JG, Jun S, Cho YW et al (2017) Deep learning in medical imaging: general overview. Korean J Radiol 18(4):570–584. https://doi.org/10.3348/kjr.2017.18.4.570

    Article  Google Scholar 

  155. Lee S, Wu P, Sun K (1998) Fractal image compression using neural networks. In: 1998 IEEE International Joint Conference on Neural Networks Proceedings. In: IEEE world congress on computational intelligence (Cat. No. 98CH36227), vol 1, pp 613–618. https://doi.org/10.1109/IJCNN.1998.682349

  156. Li J, Chen L, Cai S et al (2015) Imaging with referenceless distortion correction and flexible regions of interest using single-shot biaxial spatiotemporally encoded MRI. Neuroimage 105:93–111. https://doi.org/10.1016/j.neuroimage.2014.10.041

    Article  Google Scholar 

  157. Li P, Wang D, Wang L et al (2018) Deep visual tracking: review and experimental comparison. Pattern Recogn 76:323–338. https://doi.org/10.1016/j.patcog.2017.11.007

    Article  Google Scholar 

  158. Li S, Song W, Fang L et al (2019) Deep learning for hyperspectral image classification: an overview. IEEE Trans Geosci Remote Sens 57(9):6690–6709. https://doi.org/10.1109/TGRS.2019.2907932

    Article  Google Scholar 

  159. Li J, Gray R (1998) Text and picture segmentation by the distribution analysis of wavelet coefficients. In: Proceedings 1998 international conference on image processing. ICIP98 (Cat. No. 98CB36269), vol 3, pp 790–794. https://doi.org/10.1109/ICIP.1998.999065

  160. Li W, Mao X, Li Z (2023) Research on three-dimensional reconstruction technology of line laser scanning scene based on otsu method. In: Kountchev R, Nakamatsu K, Wang W, et al (eds) Proceedings of the world conference on intelligent and 3-D technologies (WCI3DT 2022). Springer Nature, Singapore, pp 447–458

  161. Lim S, Yap D, Manap N (2014) Medical image compression using block-based PCA algorithm. In: 2014 international conference on computer, communications, and control technology (I4CT), pp 171–175. https://doi.org/10.1109/I4CT.2014.6914169

  162. Linnainmaa S (1970) The representation of the cumulative rounding error of an algorithm as a Taylor expansion of the local rounding errors. PhD thesis, Master’s thesis (in Finnish), University Helsinki

  163. Litjens G, Kooi T, Bejnordi BE et al (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88. https://doi.org/10.1016/j.media.2017.07.005

    Article  Google Scholar 

  164. Liu X, Faes L, Kale AU et al (2019) A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digit Health 1(6):e271–e297. https://doi.org/10.1016/S2589-7500(19)30123-2

    Article  Google Scholar 

  165. Liu Y, Wang Y, Deng L et al (2019) A novel in situ compression method for CFD data based on generative adversarial network. J Vis 22(1):95–108. https://doi.org/10.1007/s12650-018-0519-x

    Article  Google Scholar 

  166. Liu X, Song L, Liu S et al (2021) A review of deep-learning-based medical image segmentation methods. Sustainability. https://doi.org/10.3390/su13031224

    Article  Google Scholar 

  167. Liu X, Zhang L, Guo Z et al (2022) Medical image compression based on variational autoencoder. Math Probl Eng 2022:7088137. https://doi.org/10.1155/2022/7088137

    Article  Google Scholar 

  168. Liu H, Chen T, Guo P, et al (2019) Non-local attention optimized deep image compression. arXiv:1904.09757

  169. Liu F, Hernandez-Cabronero M, Sanchez V, et al (2017) The current role of image compression standards in medical imaging. Information. https://doi.org/10.3390/info8040131

  170. Liu Z, Xu X, Liu T, et al (2019d) Machine vision guided 3d medical image compression for efficient transmission and accurate segmentation in the clouds. In: 2019 IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp 12679–12688. https://doi.org/10.1109/CVPR.2019.01297

  171. Lu J (1996) Image deblocking via multiscale edge processing. In: Unser MA, Aldroubi A, Laine AF (eds) Wavelet applications in signal and image processing IV, international society for optics and photonics, vol 2825. SPIE, pp 742–751. https://doi.org/10.1117/12.255282

  172. Lu Y, Xu X, Xu J (2014) Development of a hybrid manufacturing cloud. J Manuf Syst 33(4):551–566. https://doi.org/10.1016/j.jmsy.2014.05.003

  173. Lucas LFR, Rodrigues NMM, da Silva Cruz LA et al (2017) Lossless compression of medical images using 3-d predictors. IEEE Trans Med Imaging 36(11):2250–2260. https://doi.org/10.1109/TMI.2017.2714640

    Article  Google Scholar 

  174. Lundervold AS, Lundervold A (2019) An overview of deep learning in medical imaging focusing on MRI. Z Med Phys 29(2):102–127. https://doi.org/10.1016/j.zemedi.2018.11.002. Special Issue: Deep Learning in Medical Physics

  175. Luu HM, van Walsum T, Franklin D et al (2021) Efficiently compressing 3d medical images for teleinterventions via CNNs and anisotropic diffusion. Med Phys 48(6):2877–2890. https://doi.org/10.1002/mp.14814

    Article  Google Scholar 

  176. Ma H, Liu D, Xiong R et al (2020) iwave: CNN-based wavelet-like transform for image compression. IEEE Trans Multimedia 22(7):1667–1679. https://doi.org/10.1109/TMM.2019.2957990

    Article  Google Scholar 

  177. Ma S, Zhang X, Jia C et al (2020) Image and video compression with neural networks: a review. IEEE Trans Circuits Syst Video Technol 30(6):1683–1698. https://doi.org/10.1109/TCSVT.2019.2910119

    Article  Google Scholar 

  178. Ma H, Liu D, Yan N et al (2022) End-to-end optimized versatile image compression with wavelet-like transform. IEEE Trans Pattern Anal Mach Intell 44(3):1247–1263. https://doi.org/10.1109/TPAMI.2020.3026003

    Article  Google Scholar 

  179. Mahendran A, Vedaldi A (2015) Understanding deep image representations by inverting them. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR), pp 5188–5196. https://doi.org/10.1109/CVPR.2015.7299155

  180. Malik H, Farooq MS, Khelifi A et al (2020) A comparison of transfer learning performance versus health experts in disease diagnosis from medical imaging. IEEE Access 8:139367–139386. https://doi.org/10.1109/ACCESS.2020.3004766

    Article  Google Scholar 

  181. Mall PK, Singh PK, Srivastav S et al (2023) A comprehensive review of deep neural networks for medical image processing: recent developments and future opportunities. Healthc Analyt 4(100):216. https://doi.org/10.1016/j.health.2023.100216

    Article  Google Scholar 

  182. Mamoshina P, Vieira A, Putin E et al (2016) Applications of deep learning in biomedicine. Mol Pharmaceut 13(5):1445–1454. https://doi.org/10.1021/acs.molpharmaceut.5b00982

    Article  Google Scholar 

  183. Mao X, Li Q, Xie H, et al (2017) Least squares generative adversarial networks. In: Proceedings of the IEEE international conference on computer vision (ICCV)

  184. Masud M, Alhumyani H, Alshamrani SS et al (2020) Leveraging deep learning techniques for Malaria parasite detection using mobile application. Wirel Commun Mob Comput 2020:8895429. https://doi.org/10.1155/2020/8895429

    Article  Google Scholar 

  185. Mater AC, Coote ML (2019) Deep learning in chemistry. J Chem Inf Model 59(6):2545–2559. https://doi.org/10.1021/acs.jcim.9b00266

    Article  Google Scholar 

  186. Matsuda I, Mori H, Itoh S (2000) Lossless coding of still images using minimum-rate predictors. In: Proceedings 2000 international conference on image processing (Cat. No. 00CH37101), vol 1, pp 132–135. https://doi.org/10.1109/ICIP.2000.900912

  187. McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5(4):115–133. https://doi.org/10.1007/BF02478259

    Article  MathSciNet  Google Scholar 

  188. Mentzer F, Agustsson E, Tschannen M, et al (2019) Practical full resolution learned lossless image compression. In: 2019 IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp 10621–10630. https://doi.org/10.1109/CVPR.2019.01088

  189. Menze BH, Jakab A, Bauer S et al (2015) The multimodal brain tumor image segmentation benchmark (brats). IEEE Trans Med Imaging 34(10):1993–2024. https://doi.org/10.1109/TMI.2014.2377694

    Article  Google Scholar 

  190. Min S, Lee B, Yoon S (2016) Deep learning in bioinformatics. Br Bioinformat 18(5):851–869. https://doi.org/10.1093/bib/bbw068

    Article  Google Scholar 

  191. Min Q, Wang X, Huang B et al (2022) Lossless medical image compression based on anatomical information and deep neural networks. Biomed Signal Process Control 74:103499. https://doi.org/10.1016/j.bspc.2022.103499. https://www.sciencedirect.com/science/article/pii/S1746809422000210

  192. Minaee S, Boykov Y, Porikli F et al (2022) Image segmentation using deep learning: a survey. IEEE Trans Pattern Anal Mach Intell 44(7):3523–3542. https://doi.org/10.1109/TPAMI.2021.3059968

    Article  Google Scholar 

  193. Minnen D, Ballé J, Toderici GD (2018) Joint autoregressive and hierarchical priors for learned image compression. In: Bengio S, Wallach H, Larochelle H et al (eds) Advances in neural information processing systems, vol 31. Curran Associates Inc

  194. Miotto R, Wang F, Wang S et al (2017) Deep learning for healthcare: review, opportunities and challenges. Br Bioinformat 19(6):1236–1246. https://doi.org/10.1093/bib/bbx044

    Article  Google Scholar 

  195. Mishra D, Singh SK, Singh RK (2022) Deep architectures for image compression: a critical review. Signal Process 191(108):346. https://doi.org/10.1016/j.sigpro.2021.108346

    Article  Google Scholar 

  196. Mishra D, Singh SK, Singh RK (2020) Lossy medical image compression using residual learning-based dual autoencoder model. In: 2020 IEEE 7th Uttar Pradesh section international conference on electrical, electronics and computer engineering (UPCON), pp 1–5. https://doi.org/10.1109/UPCON50219.2020.9376417

  197. Moreira IC, Amaral I, Domingues I et al (2012) Inbreast: toward a full-field digital mammographic database. Acad Radiol 19(2):236–248. https://doi.org/10.1016/j.acra.2011.09.014. https://www.sciencedirect.com/science/article/pii/S107663321100451X

  198. Mozaffari J, Amirkhani A, Shokouhi SB (2023) A survey on deep learning models for detection of COVID-19. Neural Comput Appl 35(23):16945–16973. https://doi.org/10.1007/s00521-023-08683-x

    Article  Google Scholar 

  199. Mustra M, Delac K, Grgic M (2008) Overview of the dicom standard. In: 2008 50th international symposium ELMAR, pp 39–44

  200. Nadkarni PM, Ohno-Machado L, Chapman WW (2011) Natural language processing: an introduction. J Am Med Informat Assoc 18(5):544–551. https://doi.org/10.1136/amiajnl-2011-000464

    Article  Google Scholar 

  201. Nagavi TC, Mahesha P (2019) Medical image lossy compression with LSTM networks. IGI Global, Hershey, pp 47–68. Histopathological Image Analysis in Medical Decision Making. https://doi.org/10.4018/978-1-5225-6316-7.ch003

  202. Nagoor OH, Whittle J, Deng J, et al (2020) Lossless compression for volumetric medical images using deep neural network with local sampling. In: 2020 IEEE international conference on image processing (ICIP), pp 2815–2819. https://doi.org/10.1109/ICIP40778.2020.9191031

  203. Nagoor OH, Whittle J, Deng J, et al (2021) Medzip: 3d medical images lossless compressor using recurrent neural network (lstm). In: 2020 25th international conference on pattern recognition (ICPR), pp 2874–2881. https://doi.org/10.1109/ICPR48806.2021.9413341

  204. Nagoor OH, Whittle J, Deng J et al (2022) Sampling strategies for learning-based 3d medical image compression. Mach Learn Appl 8(100):273. https://doi.org/10.1016/j.mlwa.2022.100273

    Article  Google Scholar 

  205. Nickolls J, Dally WJ (2010) The GPU computing era. IEEE Micro 30(2):56–69. https://doi.org/10.1109/MM.2010.41

    Article  Google Scholar 

  206. Nilsback ME, Zisserman A (2008) Automated flower classification over a large number of classes. In: 2008 6th Indian conference on computer vision, graphics and image processing, pp 722–729. https://doi.org/10.1109/ICVGIP.2008.47

  207. Nirthika R, Manivannan S, Ramanan A et al (2022) Pooling in convolutional neural networks for medical image analysis: a survey and an empirical study. Neural Comput Appl 34(7):5321–5347. https://doi.org/10.1007/s00521-022-06953-8

    Article  Google Scholar 

  208. Nivedha B, Priyadharshini M, Thendral E, et al (2017) Lossless image compression in cloud computing. In: 2017 international conference on technical advancements in computers and communications (ICTACC), pp 112–115. https://doi.org/10.1109/ICTACC.2017.37

  209. NVIDIA (1999) Nvidia launches the world’s first graphics processing unit: Geforce 256. https://web.archive.org/web/20160412035751/https://www.nvidia.com/object/IO_20020111_5424.html

  210. Otazo R, Lambin P, Pignol JP et al (2021) MRI-guided radiation therapy: an emerging paradigm in adaptive radiation oncology. Radiology 298(2):248–260. https://doi.org/10.1148/radiol.2020202747

    Article  Google Scholar 

  211. Otter DW, Medina JR, Kalita JK (2021) A survey of the usages of deep learning for natural language processing. IEEE Trans Neural Netw Learn Syst 32(2):604–624. https://doi.org/10.1109/TNNLS.2020.2979670

    Article  MathSciNet  Google Scholar 

  212. Owens JD, Houston M, Luebke D et al (2008) GPU computing. Proc IEEE 96(5):879–899. https://doi.org/10.1109/JPROC.2008.917757

    Article  Google Scholar 

  213. Pace DF, Dalca AV, Geva T et al (2015) Interactive whole-heart segmentation in congenital heart disease. In: Navab N, Hornegger J, Wells WM et al (eds) Medical image computing and computer-assisted intervention—MICCAI 2015. Springer, Cham, pp 80–88

    Google Scholar 

  214. Parracho JO, Thomaz LA, Távora LMN, et al (2021) Cross-modality lossless compression of pet-ct images. In: 2021 telecoms conference (ConfTELE), pp 1–6. https://doi.org/10.1109/ConfTELE50222.2021.9435467

  215. Patel MI, Suthar S, Thakar J (2019) Survey on image compression using machine learning and deep learning. In: 2019 international conference on intelligent computing and control systems (ICCS), pp 1103–1105. https://doi.org/10.1109/ICCS45141.2019.9065473

  216. Penedo M, Souto M, Tahoces PG et al (2005) Free-response receiver operating characteristic evaluation of lossy jpeg2000 and object-based set partitioning in hierarchical trees compression of digitized mammograms. Radiology 237(2):450–457. https://doi.org/10.1148/radiol.2372040996

    Article  Google Scholar 

  217. Rabbani M (2002) JPEG2000: image compression fundamentals, standards and practice. J Electron Imaging 11(2):286. https://doi.org/10.1117/1.1469618

    Article  Google Scholar 

  218. Rahman A et al (2019) Improving malaria parasite detection from red blood cell using deep convolutional neural networks. arXiv preprint arXiv:1907.10418

  219. Raj A, Sathish R, Sarkar T et al (2023) Designing deep neural high-density compression engines for radiology images. Circuits, Syst, Signal Process 42(2):643–682. https://doi.org/10.1007/s00034-022-02222-0

    Article  Google Scholar 

  220. Rajpurkar P, Irvin J, Bagul A, et al (2017) MURA: large dataset for abnormality detection in musculoskeletal radiographs. arXiv e-prints arXiv:1712.06957

  221. Ranjan R, Sankaranarayanan S, Bansal A et al (2018) Deep learning for understanding faces: machines may be just as good, or better, than humans. IEEE Signal Process Mag 35(1):66–83. https://doi.org/10.1109/MSP.2017.2764116

    Article  Google Scholar 

  222. Rasti B, Scheunders P, Ghamisi P et al (2018) Noise reduction in hyperspectral imagery: overview and application. Remote Sens. https://doi.org/10.3390/rs10030482

    Article  Google Scholar 

  223. Rasti B, Chang Y, Dalsasso E et al (2022) Image restoration for remote sensing: overview and toolbox. IEEE Geosci Remote Sens Mag 10(2):201–230. https://doi.org/10.1109/MGRS.2021.3121761

    Article  Google Scholar 

  224. Ravikiran HK, Jayanth J (2019) Medical image compression using neural network with HGAPSO optimization. Int J Innov Technol Explor Eng 9:3505–3509. https://doi.org/10.35940/ijitee.B6616.129219

    Article  Google Scholar 

  225. Razzak MI, Naz S, Zaib A (2018) Deep learning for medical image processing: overview, challenges and the future. Springer, Cham, pp 323–350. https://doi.org/10.1007/978-3-319-65981-7_12

  226. Reddy BV, Reddy PB, Kumar PS, et al (2016) Lossless compression of medical images for better diagnosis. In: 2016 IEEE 6th international conference on advanced computing (IACC), pp 404–408.https://doi.org/10.1109/IACC.2016.81

  227. Refai A, Merouani HF, Aouras H (2016) Maintenance of a Bayesian network: application using medical diagnosis. Evol Syst 7(3):187–196. https://doi.org/10.1007/s12530-016-9146-8

    Article  Google Scholar 

  228. Rhee H, Jang YI, Kim S, et al (2020) Channel-wise progressive learning for lossless image compression. In: 2020 IEEE international conference on image processing (ICIP), pp 1113–1117. https://doi.org/10.1109/ICIP40778.2020.9191322

  229. Ricci P, Gavryusev V, Müllenbroich C et al (2022) Removing striping artifacts in light-sheet fluorescence microscopy: a review. Progr Biophys Mol Biol 168:52–65. https://doi.org/10.1016/j.pbiomolbio.2021.07.003

    Article  Google Scholar 

  230. Robb RA (2009) Visualization. In: Bankman IN (ed) Handbook of medical image processing and analysis, 2nd edn. Academic Press, Burlington, pp 725–727. https://doi.org/10.1016/B978-012373904-9.50054-4. https://www.sciencedirect.com/science/article/pii/B9780123739049500544

  231. Rohmetra H, Raghunath N, Narang P et al (2023) AI-enabled remote monitoring of vital signs for COVID-19: methods, prospects and challenges. Computing 105(4):783–809. https://doi.org/10.1007/s00607-021-00937-7

    Article  Google Scholar 

  232. Rosenblatt F (1957) The perceptron, a perceiving and recognizing automaton Project Para. Cornell Aeronautical Laboratory

  233. Routray SK, Javali A, Sharmila KP, et al (2020) Lossless compression techniques for low bandwidth networks. In: 2020 3rd international conference on intelligent sustainable systems (ICISS), pp 823–828. https://doi.org/10.1109/ICISS49785.2020.9315936

  234. Rumelhart DE, Hinton GE, Williams RJ (1985) Learning internal representations by error propagation. Tech. rep, California University San Diego La Jolla Inst for Cognitive Science

  235. Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323(6088):533–536. https://doi.org/10.1038/323533a0

    Article  Google Scholar 

  236. Saadi H, Merouani HF, Melouah A et al (2022) Multi-agents system for breast tumour detection in mammography by deep learning pre-processing and watershed segmentation. Int J Comput Vis Robot 12(6):632–661. https://doi.org/10.1504/IJCVR.2022.126506

    Article  Google Scholar 

  237. Safa G, Akila D, Farida MH (2022) A survey on hybrid case-based reasoning and deep learning systems for medical data classification. In: Advances in business information systems and analytics. IGI Global, pp 113–141. https://doi.org/10.4018/978-1-7998-9016-4.ch006

  238. Sah M, Direkoglu C (2022) A survey of deep learning methods for multiple sclerosis identification using brain MRI images. Neural Comput Appl 34(10):7349–7373. https://doi.org/10.1007/s00521-022-07099-3

    Article  Google Scholar 

  239. Said A, Pearlman W (1996) A new, fast, and efficient image codec based on set partitioning in hierarchical trees. IEEE Trans Circuits Syst Video Technol 6(3):243–250. https://doi.org/10.1109/76.499834

    Article  Google Scholar 

  240. Salomon D, Motta G (2010) Handbook of data compression, 5th edn. Springer, London. https://doi.org/10.1007/978-1-84882-903-9

  241. Sampat MP, Wang Z, Gupta S et al (2009) Complex wavelet structural similarity: a new image similarity index. IEEE Trans Image Process 18(11):2385–2401. https://doi.org/10.1109/TIP.2009.2025923

    Article  MathSciNet  Google Scholar 

  242. Santurkar S, Budden D, Shavit N (2018) Generative compression. In: 2018 picture coding symposium (PCS), pp 258–262. https://doi.org/10.1109/PCS.2018.8456298

  243. Saremi S, Mirjalili S, Lewis A (2015) How important is a transfer function in discrete heuristic algorithms. Neural Comput Appl 26(3):625–640. https://doi.org/10.1007/s00521-014-1743-5

    Article  Google Scholar 

  244. Sarvamangala DR, Kulkarni RV (2022) Convolutional neural networks in medical image understanding: a survey. Evol Intell 15(1):1–22. https://doi.org/10.1007/s12065-020-00540-3

    Article  Google Scholar 

  245. Schelkens P, Munteanu A, Tzannes A, et al (2006) Jpeg2000. Part 10. Volumetric data encoding. In: 2006 IEEE international symposium on circuits and systems, p 3877. https://doi.org/10.1109/ISCAS.2006.1693474

  246. Schiopu I, Munteanu A (2020) Deep-learning-based lossless image coding. IEEE Trans Circuits Syst Video Technol 30(7):1829–1842. https://doi.org/10.1109/TCSVT.2019.2909821

    Article  Google Scholar 

  247. Schiopu I, Huang H, Munteanu A (2020) CNN-based intra-prediction for lossless HEVC. IEEE Trans Circuits Syst Video Technol 30(7):1816–1828. https://doi.org/10.1109/TCSVT.2019.2940092

    Article  Google Scholar 

  248. Schiopu I, Munteanu A (2020b) A study of prediction methods based on machine learning techniques for lossless image coding. In: 2020 IEEE international conference on image processing (ICIP), pp 3324–3328. https://doi.org/10.1109/ICIP40778.2020.9190696

  249. Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117

    Article  Google Scholar 

  250. Seeram E (2006) Irreversible compression in digital radiology. A literature review. Radiography 12(1):45–59. https://doi.org/10.1016/j.radi.2005.04.002

    Article  Google Scholar 

  251. Selvaraju RR, Cogswell M, Das A, et al (2017) Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE international conference on computer vision, pp 618–626

  252. Setio AAA et al (2017) Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge. Med Image Anal 42:1–13. https://doi.org/10.1016/j.media.2017.06.015

    Article  Google Scholar 

  253. Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27(3):379–423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x

    Article  MathSciNet  Google Scholar 

  254. Sheibanifard A, Yu H (2023) A novel implicit neural representation for volume data. Appl Sci. https://doi.org/10.3390/app13053242

    Article  Google Scholar 

  255. Shen D, Wu G, Suk HI (2017) Deep learning in medical image analysis. Ann Rev Biomed Eng 19(1):221–248. https://doi.org/10.1146/annurev-bioeng-071516-044442

    Article  Google Scholar 

  256. Shen H, Pan WD, Dong Y, et al (2016) Lossless compression of curated erythrocyte images using deep autoencoders for malaria infection diagnosis. In: 2016 picture coding symposium (PCS). IEEE, pp 1–5

  257. SHI X, Chen Z, Wang H, et al (2015) Convolutional lstm network: A machine learning approach for precipitation nowcasting. In: Cortes C, Lawrence N, Lee D, et al (eds) Advances in neural information processing systems, vol 28. Curran Associates, Inc. https://proceedings.neurips.cc/paper_files/paper/2015/file/07563a3fe3bbe7e3ba84431ad9d055af-Paper.pdf

  258. Shin HC, Roth HR, Gao M et al (2016) Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 35(5):1285–1298. https://doi.org/10.1109/TMI.2016.2528162

    Article  Google Scholar 

  259. Shinde PP, Shah S (2018) A review of machine learning and deep learning applications. In: 2018 4th international conference on computing communication control and automation (ICCUBEA), pp 1–6. https://doi.org/10.1109/ICCUBEA.2018.8697857

  260. Shorfuzzaman M (2023) IoT-enabled stacked ensemble of deep neural networks for the diagnosis of COVID-19 using chest CT scans. Computing 105(4):887–908. https://doi.org/10.1007/s00607-021-00971-5

    Article  Google Scholar 

  261. Shukla S, Srivastava A (2018) Medical images compression using convolutional neural network with LWT. Int J Mod Commun Technol Res 6(6):9–12

    Google Scholar 

  262. Silva E, Breslau J, Barr RM et al (2013) ACR white paper on teleradiology practice: a report from the task force on teleradiology practice. J Am Coll Radiol 10(8):575–585. https://doi.org/10.1016/j.jacr.2013.03.018

    Article  Google Scholar 

  263. Simpson AL, Leal JN, Pugalenthi A et al (2015) Chemotherapy-induced splenic volume increase is independently associated with major complications after hepatic resection for metastatic colorectal cancer. J Am Coll Surg 220(3):271–280. https://doi.org/10.1016/j.jamcollsurg.2014.12.008

    Article  Google Scholar 

  264. Singer D, Clark R, Lee D (2004) MIME type registrations for JPEG 2000 (ISO/IEC 15444). Tech. rep. https://doi.org/10.17487/rfc3745

  265. Sitzmann V, Martel J, Bergman A, et al (2020) Implicit neural representations with periodic activation functions. In: Larochelle H, Ranzato M, Hadsell R, et al (eds) Advances in neural information processing systems, vol 33. Curran Associates, Inc., pp 7462–7473

  266. Soliman HS, Omari M (2006) A neural networks approach to image data compression. Appl Soft Comput 6(3):258–271. https://doi.org/10.1016/j.asoc.2004.12.006

    Article  Google Scholar 

  267. Sridhar C, Pareek PK, Kalidoss R et al (2022) Optimal medical image size reduction model creation using recurrent neural network and genpsowvq. J Healthc Eng 2354:866. https://doi.org/10.1155/2022/2354866

    Article  Google Scholar 

  268. Stamm MC, Liu KR (2011) Anti-forensics of digital image compression. IEEE Trans Inf Forens Secur 6(3):1050–1065. https://doi.org/10.1109/TIFS.2011.2119314

    Article  Google Scholar 

  269. Suetens P (2017) Fundamentals of medical imaging, 3rd edn. Cambridge University Press. https://doi.org/10.1017/9781316671849

  270. Sullivan GJ, Ohm JR, Han WJ et al (2012) Overview of the high efficiency video coding (HEVC) standard. IEEE Trans Circuits Syst Video Technol 22(12):1649–1668. https://doi.org/10.1109/TCSVT.2012.2221191

    Article  Google Scholar 

  271. Sushmit AS, Zaman SU, Humayun AI, et al (2019) X-ray image compression using convolutional recurrent neural networks. In: 2019 IEEE EMBS international conference on biomedical and Health Informat (BHI), pp 1–4. https://doi.org/10.1109/BHI.2019.8834656

  272. Suzuki K (2017) Overview of deep learning in medical imaging. Radiol Phys Technol 10(3):257–273. https://doi.org/10.1007/s12194-017-0406-5

    Article  Google Scholar 

  273. Sweldens W (1995) Lifting scheme: a new philosophy in biorthogonal wavelet constructions. In: Laine AF, Unser MA (eds) Wavelet applications in signal and image processing III, international society for optics and photonics, vol 2569. SPIE, pp 68–79. https://doi.org/10.1117/12.217619

  274. Sweldens W (1996) The lifting scheme: a custom-design construction of biorthogonal wavelets. Appl Comput Harmon Anal 3(2):186–200. https://doi.org/10.1006/acha.1996.0015

    Article  MathSciNet  Google Scholar 

  275. Sweldens W (1998) The lifting scheme: a construction of second generation wavelets. SIAM J Math Anal 29(2):511–546. https://doi.org/10.1137/S0036141095289051

    Article  MathSciNet  Google Scholar 

  276. Takemura S, Xu CS, Lu Z et al (2015) Synaptic circuits and their variations within different columns in the visual system of “drosophila’’. Proc Natl Acad Sci 112(44):13711–13716. https://doi.org/10.1073/pnas.1509820112

    Article  Google Scholar 

  277. Tellez D, Litjens G, van der Laak J et al (2021) Neural image compression for gigapixel histopathology image analysis. IEEE Trans Pattern Anal Mach Intell 43(2):567–578. https://doi.org/10.1109/TPAMI.2019.2936841

    Article  Google Scholar 

  278. Theis L, Shi W, Cunningham A, et al (2017) Lossy image compression with compressive autoencoders. In: International conference on learning representations

  279. Tobon-Gomez C, Geers AJ, Peters J et al (2015) Benchmark for algorithms segmenting the left atrium from 3d CT and MRI datasets. IEEE Trans Med Imaging 34(7):1460–1473. https://doi.org/10.1109/TMI.2015.2398818

    Article  Google Scholar 

  280. Toderici G, Vincent D, Johnston N, et al (2017) Full resolution image compression with recurrent neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR). https://doi.org/10.1109/CVPR.2017.577

  281. Tong T, Li G, Liu X, et al (2017) Image super-resolution using dense skip connections. In: Proceedings of the IEEE international conference on computer vision (ICCV)

  282. Townsend J, Bird T, Barber D (2019) Practical lossless compression with latent variables using bits back coding. CoRR arXiv:1901.04866

  283. Veta M et al (2019) Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge. Med Image Anal 54:111–121. https://doi.org/10.1016/j.media.2019.02.012

    Article  Google Scholar 

  284. Udupa JK, Herman GT (1999) 3D imaging in medicine. CRC Press

  285. Umer M, Sadiq S, Ahmad M et al (2020) A novel stacked CNN for malarial parasite detection in thin blood smear images. IEEE Access 8:93782–93792. https://doi.org/10.1109/ACCESS.2020.2994810

    Article  Google Scholar 

  286. van den Oord A, Kalchbrenner N, Kavukcuoglu K (2016) Pixel recurrent neural networks. In: Balcan MF, Weinberger KQ (eds) Proceedings of the 33rd international conference on machine learning, proceedings of machine learning research, vol 48. PMLR, pp 1747–1756

  287. Voulodimos A, Doulamis N, Doulamis A et al (2018) Deep learning for computer vision: a brief review. Comput Intell Neurosci 2018:7068349. https://doi.org/10.1155/2018/7068349

    Article  Google Scholar 

  288. Wallace G (1992) The jpeg still picture compression standard. IEEE Trans Consum Electron 38(1):xviii–xxxiv. https://doi.org/10.1109/30.125072

  289. Wallace GK (1991) The jpeg still picture compression standard. Commun ACM 34(4):30–44. https://doi.org/10.1145/103085.103089

    Article  Google Scholar 

  290. Wang Z, Bovik A, Sheikh H et al (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612. https://doi.org/10.1109/TIP.2003.819861

    Article  Google Scholar 

  291. Wang H, Lei Z, Zhang X et al (2019) A review of deep learning for renewable energy forecasting. Energy Convers Manag 198(111):799. https://doi.org/10.1016/j.enconman.2019.111799

    Article  Google Scholar 

  292. Wang H, Zhu Q, Ding L et al (2019) Scalable volumetric imaging for ultrahigh-speed brain mapping at synaptic resolution. Natl Sci Rev 6(5):982–992. https://doi.org/10.1093/nsr/nwz053. https://academic.oup.com/nsr/article-pdf/6/5/982/38916472/nwz053.pdf

  293. Wang N, Wang Y, Er MJ (2022) Review on deep learning techniques for marine object recognition: architectures and algorithms. Control Eng Pract 118(104):458. https://doi.org/10.1016/j.conengprac.2020.104458

    Article  Google Scholar 

  294. Wang Z, Bovik A, Evan B (2000) Blind measurement of blocking artifacts in images. In: Proceedings 2000 international conference on image processing (Cat. No. 00CH37101), vol. 3, pp 981–984. https://doi.org/10.1109/ICIP.2000.899622

  295. Wang J, Huang H (2000) 52—three-dimensional image compression with wavelet transforms. In: Bankman IN (ed) Handbook of medical imaging. Biomedical engineering. Academic Press, San Diego, pp 851–862. https://doi.org/10.1016/B978-012077790-7/50060-6

  296. Wang Z, Liu D, Chang S, et al (2016) D3: deep dual-domain based fast restoration of jpeg-compressed images. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 2764–2772. https://doi.org/10.1109/CVPR.2016.302

  297. Wang X, Peng Y, Lu L, et al (2017) Chestx-ray8: hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR), pp 3462–3471. https://doi.org/10.1109/CVPR.2017.369

  298. Wang Z, Simoncelli E, Bovik A (2003) Multiscale structural similarity for image quality assessment. In: The 37th Asilomar conference on signals, systems and computers, vol 2, pp 1398–1402. https://doi.org/10.1109/ACSSC.2003.1292216

  299. Weinberger M, Seroussi G, Sapiro G (2000) The loco-i lossless image compression algorithm: principles and standardization into jpeg-ls. IEEE Trans Image Process 9(8):1309–1324. https://doi.org/10.1109/83.855427

    Article  Google Scholar 

  300. Winzeck S et al (2018) ISLES 2016 and 2017-Benchmarking ischemic stroke lesion outcome prediction based on multispectral MRI. Front Neurol. https://doi.org/10.3389/fneur.2018.00679

    Article  Google Scholar 

  301. Wolfswinkel JF, Furtmueller E, Wilderom CP (2013) Using grounded theory as a method for rigorously reviewing literature. Eur J Inf Syst 22(1):45–55

    Article  Google Scholar 

  302. Wong A, Lou S (2000) 47 - medical image archive, retrieval, and communication. In: Bankman IN (ed) Handbook of medical imaging. Biomedical engineering. Academic Press, San Diego, pp 771–781. https://doi.org/10.1016/B978-012077790-7/50055-2

  303. Woznitza N, Piper K, Burke S et al (2018) Chest x-ray interpretation by radiographers is not inferior to radiologists: a multireader, multicase comparison using jafroc (jack-knife alternative free-response receiver operating characteristics) analysis. Acad Radiol 25(12):1556–1563. https://doi.org/10.1016/j.acra.2018.03.026

    Article  Google Scholar 

  304. Wu YG (2002) Medical image compression by sampling dct coefficients. IEEE Trans Inf Technol Biomed 6(1):86–94. https://doi.org/10.1109/4233.992167

    Article  Google Scholar 

  305. Wu X, Memon N (2000) Context-based lossless interband compression-extending calic. IEEE Trans Image Process 9(6):994–1001

    Article  Google Scholar 

  306. Wu W, Miller KL (2017) Image formation in diffusion MRI: a review of recent technical developments. J Magnet Reson Imaging 46(3):646–662. https://doi.org/10.1002/jmri.25664

    Article  Google Scholar 

  307. Wu X, Memon N (1996) Calic-a context based adaptive lossless image codec. In: 1996 IEEE international conference on acoustics, speech, and signal processing conference proceedings, vol 4, pp 1890–1893. https://doi.org/10.1109/ICASSP.1996.544819

  308. Wu D, Rosen DW, Schaefer D (2014) Cloud-based design and manufacturing: status and promise. Springer, Cham, pp 1–24. https://doi.org/10.1007/978-3-319-07398-9_1

  309. Xing-Yuan W, Fan-Ping L, Shu-Guo W (2009) Fractal image compression based on spatial correlation and hybrid genetic algorithm. J Vis Commun Image Represent 20(8):505–510. https://doi.org/10.1016/j.jvcir.2009.07.002

    Article  Google Scholar 

  310. Xu XG (2014) An exponential growth of computational phantom research in radiation protection, imaging, and radiotherapy: a review of the fifty-year history. Phys Med Biol. https://doi.org/10.1088/0031-9155/59/18/R233

    Article  Google Scholar 

  311. Xu S, Chang CC, Liu Y (2021) A novel image compression technology based on vector quantisation and linear regression prediction. Connect Sci 33(2):219–236. https://doi.org/10.1080/09540091.2020.1806206

    Article  Google Scholar 

  312. Xue D, Ma H, Li L et al (2023) aiwave: volumetric image compression with 3-d trained affine wavelet-like transform. IEEE Trans Med Imaging 42(3):606–618. https://doi.org/10.1109/TMI.2022.3212780

    Article  Google Scholar 

  313. Yang F, Mou J, Sun K et al (2020) Lossless image compression-encryption algorithm based on BP neural network and chaotic system. Multimedia Tools Appl 79(27):19963–19992. https://doi.org/10.1007/s11042-020-08821-w

    Article  Google Scholar 

  314. Yapp KE, Suleiman M, Brennan P et al (2023) Periapical radiography versus cone beam computed tomography in endodontic disease detection: a free-response, factorial study. J Endod 49(4):419–429. https://doi.org/10.1016/j.joen.2023.02.001

    Article  Google Scholar 

  315. Yeo WK, Yap DFW, Oh T, et al (2011) Grayscale medical image compression using feedforward neural networks. In: 2011 IEEE international conference on computer applications and industrial electronics (ICCAIE), pp 633–638. https://doi.org/10.1109/ICCAIE.2011.6162211

  316. Yosinski J et al (2015) Understanding neural networks through deep visualization. https://doi.org/10.48550/arXiv.1506.06579

  317. Young T, Hazarika D, Poria S et al (2018) Recent trends in deep learning based natural language processing [review article]. IEEE Comput Intell Mag 13(3):55–75. https://doi.org/10.1109/MCI.2018.2840738

    Article  Google Scholar 

  318. Yu H, Yang LT, Zhang Q et al (2021) Convolutional neural networks for medical image analysis: state-of-the-art, comparisons, improvement and perspectives. Neurocomputing 444:92–110. https://doi.org/10.1016/j.neucom.2020.04.157

    Article  Google Scholar 

  319. Zaidi SSA, Ansari MS, Aslam A et al (2022) A survey of modern deep learning based object detection models. Digit Signal Process 126(103):514. https://doi.org/10.1016/j.dsp.2022.103514

    Article  Google Scholar 

  320. Zalis ME, Hahn PF, Arellano RS et al (2001) Ct colonography with teleradiology: effect of lossy wavelet compression on polyp detection-initial observations. Radiology 220(2):387–392. https://doi.org/10.1148/radiology.220.2.r01au33387

    Article  Google Scholar 

  321. Zhang W, Hasegawa A, Itoh K et al (1991) Image processing of human corneal endothelium based on a learning network. Appl Opt 30(29):4211–4217. https://doi.org/10.1364/AO.30.004211

    Article  Google Scholar 

  322. Zhang Y, Lin K (2024) End-to-end optimized image compression with the frequency-oriented transform. Mach Vis Appl 35(2):27

    Article  Google Scholar 

  323. Zhang K, Zuo W, Chen Y et al (2017) Beyond a gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans Image Process 26(7):3142–3155. https://doi.org/10.1109/TIP.2017.2662206

    Article  MathSciNet  Google Scholar 

  324. Zhang Y, Shi X, Zhang H et al (2022) Review on deep learning applications in frequency analysis and control of modern power system. Int J Electr Power Energy Syst 136(107):744. https://doi.org/10.1016/j.ijepes.2021.107744

    Article  Google Scholar 

  325. Zhang W, Doi K, Giger ML et al (1994) Computerized detection of clustered microcalcifications in digital mammograms using a shift-invariant artificial neural network. Med Phys 21(4):517–524. https://doi.org/10.1118/1.597177

  326. Zhang W, Hasegawa A, Matoba O, et al (1992) Shift-invariant neural network for image processing: learning and generalization. In: Rogers SK (ed) Applications of artificial neural networks III, international society for optics and photonics, vol 1709. SPIE, pp 257–268. https://doi.org/10.1117/12.140004

  327. Zhang W, Tanida J, Itoh K, et al (1988) Shift-invariant pattern recognition neural network and its optical architecture. In: Proceedings of annual conference of the Japan Society of Applied Physics, Montreal, CA, pp 2147–2151

  328. Zhao B, James LP, Moskowitz CS et al (2009) Evaluating variability in tumor measurements from same-day repeat ct scans of patients with non-small cell lung cancer. Radiology 252(1):263–272. https://doi.org/10.1148/radiol.2522081593. (pMID: 19561260)

    Article  Google Scholar 

  329. Zhao B, Schwartz LH, Kris MG (2015) Rider_lung ct. Cancer Imaging Arch. https://doi.org/10.7937/K9/TCIA.2015.U1X8A5NR

    Article  Google Scholar 

  330. Zhao ZQ, Zheng P, Xu ST et al (2019) Object detection with deep learning: a review. IEEE Trans Neural Netw Learn Syst 30(11):3212–3232. https://doi.org/10.1109/TNNLS.2018.2876865

    Article  Google Scholar 

  331. Zhao C, Li H, Jiao Z et al (2020) A 3d convolutional encapsulated long short-term memory (3dconv-lstm) model for denoising FMRI data. In: Martel AL, Abolmaesumi P, Stoyanov D et al (eds) Medical image computing and computer assisted intervention—MICCAI 2020. Springer, Cham, pp 479–488

    Google Scholar 

  332. Zhao D, Li J, Li H et al (2023) Stripe sensitive convolution for omnidirectional image dehazing. IEEE Trans Vis Comput Graph. https://doi.org/10.1109/TVCG.2022.3233900

    Article  Google Scholar 

  333. Zheng Z, Lauritzen JS, Perlman E et al (2018) A complete electron microscopy volume of the brain of adult drosophila melanogaster. Cell 174(3):730-743.e22. https://doi.org/10.1016/j.cell.2018.06.019

    Article  Google Scholar 

  334. Zhou L, Zhang C, Liu F et al (2019) Application of deep learning in food: a review. Compr Rev Food Sci Food Saf 18(6):1793–1811

    Article  Google Scholar 

  335. Zhou SK, Greenspan H, Davatzikos C et al (2021) A review of deep learning in medical imaging: imaging traits, technology trends, case studies with progress highlights, and future promises. Proc IEEE 109(5):820–838. https://doi.org/10.1109/JPROC.2021.3054390

    Article  Google Scholar 

  336. Zhu Y, Yuan J (2014) A bit allocation optimization method for roi based image compression with stable image quality. In: 2014 22nd international conference on pattern recognition, pp 849–854. https://doi.org/10.1109/ICPR.2014.156

  337. Zou Z, Chen K, Shi Z et al (2023) Object detection in 20 years: a survey. Proc IEEE 111(3):257–276. https://doi.org/10.1109/JPROC.2023.3238524

    Article  Google Scholar 

  338. Zuo C, Qian J, Feng S et al (2022) Deep learning in optical metrology: a review. Light: Sci Appl 11(1):39. https://doi.org/10.1038/s41377-022-00714-x

Download references

Funding

Not applicable

Author information

Author notes

  1. Hayet Farida Merouani and Akila Djebbar contributed equally to this work.

Authors and Affiliations

  1. LRI Laboratory, Computer Science Department, Badji Mokhtar University, Sidi Amar, Annaba, 23005, Annaba, Algeria

    Nour El Houda Bourai, Hayet Farida Merouani & Akila Djebbar

Authors
  1. Nour El Houda Bourai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hayet Farida Merouani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Akila Djebbar
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

These authors contributed equally to this work.

Corresponding author

Correspondence to Nour El Houda Bourai.

Ethics declarations

Conflict of interest

We the authors declare that there is no conflict of interest in our manuscript.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bourai, N.E.H., Merouani, H.F. & Djebbar, A. Deep learning-assisted medical image compression challenges and opportunities: systematic review. Neural Comput & Applic (2024). https://doi.org/10.1007/s00521-024-09660-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00521-024-09660-8

Keywords

Search

Navigation