Skip to main content
SpringerLink
Log in

A Survey of Deep Learning Techniques for Medical Diagnosis

  • Conference paper
  • First Online:
Information and Communication Technology for Sustainable Development

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 933))

Abstract

With the advent of new technologies in artificial intelligence and machine learning, the medical community has taken a strong notice of the potential of these technologies for addressing automation. Deep learning is one of these technologies which has been chosen by the research community for advancing its medical applications. This survey paper serves the research community twofold. First, it gives researchers an introduction to the basic technologies involved in deep learning. Second, it gives the readers insight into the state of the art in the field of medical applications of deep learning, particularly for medical imaging technologies.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)

    Article  Google Scholar 

  2. MIT Technology Review identifies the 10 most important technology milestones of the past year, M.I.T. Technology Review (23 April, 2013). online: https://www.technologyreview.com/s/513981/the-10-breakthroughtechnologies-of-2013/

  3. Razzak, M.I., Naz, S., Zaib, A.: Deep learning for medical image processing: overview, challenges and the future. In: Classification in BioApps, pp. 323–350. Springer (2018)

    Google Scholar 

  4. Selvathi, D., Poornila, A.A.: Deep learning techniques for breast cancer detection using medical image analysis. In: Biologically Rationalized Computing Techniques for Image Processing Applications, pp. 159–186. Springer (2018)

    Google Scholar 

  5. Litjens, G., Kooi, T., Bejnordi, B.E., Setio, A.A.A., Ciompi, F., Ghafoorian, M., van der Laak, J.A., van Ginneken, B., Sánchez, C.I.: A survey on deep learning in medical image analysis. arXiv preprint arXiv:170205747 (2017)

  6. Greenspan, H., van Ginneken, B., Summers, R.M.: Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique. IEEE Trans. Med. Imaging 35(5), 1153–1159 (2016)

    Article  Google Scholar 

  7. Liu, J., Pan, Y., Li, M., Chen, Z., Tang, L., Lu, C., Wang, J.: Applications of deep learning to MRI images: a survey. Big Data Mining Anal. 1(1), 1–18 (2018)

    Article  Google Scholar 

  8. Yoo, Y., Tang, L.Y., Brosch, T., Li, D.K., Kolind, S., Vavasour, I., Rauscher, A., MacKay, A.L., Traboulsee, A., Tam, R.C.: Deep learning of joint myelin and T1w MRI features in normal-appearing brain tissue to distinguish between multiple sclerosis patients and healthy controls. NeuroImage Clin. 17, 169–178 (2018)

    Article  Google Scholar 

  9. Avendi, M., Kheradvar, A., Jafarkhani, H.: A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI. Med. Image Anal. 30, 108–119 (2016)

    Article  Google Scholar 

  10. Ben-Cohen, A., Diamant, I., Klang, E., Amitai, M., Greenspan, H.: Fully convolutional network for liver segmentation and lesions detection. In: International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, pp. 77–85. Springer (2016)

    Google Scholar 

  11. Birenbaum, A., Greenspan, H.: Longitudinal multiple sclerosis lesion segmentation using multi-view convolutional neural networks. In: International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, pp. 58–67. Springer (2016)

    Google Scholar 

  12. de Brebisson, A., Montana, G.: Deep neural networks for anatomical brain segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 20–28 (2015)

    Google Scholar 

  13. Drozdzal, M., Vorontsov, E., Chartrand, G., Kadoury, S., Pal, C.: The importance of skip connections in biomedical image segmentation. In: International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, pp. 179–187. Springer (2016)

    Google Scholar 

  14. Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., Pal, C., Jodoin, P.-M., Larochelle, H.: Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18–31 (2017)

    Article  Google Scholar 

  15. Pereira, S., Pinto, A., Alves, V., Silva, C.A.: Deep convolutional neural networks for the segmentation of gliomas in multi-sequence MRI. In: International Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, pp. 131–143. Springer (2015)

    Google Scholar 

  16. Pereira, S., Pinto, A., Alves, V., Silva, C.A.: Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans. Med. Imaging 35(5), 1240–1251 (2016)

    Article  Google Scholar 

  17. Zhang, W., Li, R., Deng, H., Wang, L., Lin, W., Ji, S., Shen, D.: Deep convolutional neural networks for multi-modality isointense infant brain image segmentation. NeuroImage 108, 214–224 (2015)

    Article  Google Scholar 

  18. Zhou, X., Ito, T., Takayama, R., Wang, S., Hara, T., Fujita, H.: Three-dimensional CT image segmentation by combining 2D fully convolutional network with 3D majority voting. In: International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, pp. 111–120. Springer (2016)

    Google Scholar 

  19. Bahrami, K., Shi, F., Rekik, I., Shen, D.: Convolutional neural network for reconstruction of 7 T-like images from 3 T MRI using appearance and anatomical features. In: International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, pp. 39–47. Springer (2016)

    Google Scholar 

  20. Nie, D., Cao, X., Gao, Y., Wang, L., Shen, D.: Estimating CT image from MRI data using 3D fully convolutional networks. In: International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, pp. 170–178. Springer (2016)

    Google Scholar 

  21. Li, R., Zhang, W., Suk, H.-I., Wang, L., Li, J., Shen, D., Ji, S.: Deep learning based imaging data completion for improved brain disease diagnosis. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 305–312. Springer (2014)

    Google Scholar 

  22. Heaton, J.: Artificial Intelligence for Humans, Volume 3: Deep Learning and Neural Networks. Heaton Research. Inc., St Louis, MO, USA (2015)

    Google Scholar 

  23. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)

    Article  Google Scholar 

  24. Carneiro, G., Mateus, D., Peter, L., Bradley, A., Tavares, J.M.R., Belagiannis, V., Papa, J.P., Nascimento, J.C., Loog, M., Lu, Z.: Deep Learning and Data Labeling for Medical Applications: First International Workshop, LABELS 2016, and Second International Workshop, DLMIA 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 21, 2016, Proceedings, vol. 10008. Springer (2016)

    Google Scholar 

  25. Han, X.-H., Lei, J., Chen, Y.-W.: HEp-2 cell classification using K-support spatial pooling in deep CNNs. In: International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, pp. 3–11. Springer (2016)

    Google Scholar 

  26. Akram, S.U., Kannala, J., Eklund, L., Heikkilä, J.: Cell segmentation proposal network for microscopy image analysis. In: International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, pp. 21–29. Springer (2016)

    Google Scholar 

  27. Shelhamer, E., Long, J., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640–651 (2017)

    Article  Google Scholar 

  28. Akram, S.U., Kannala, J., Eklund, L., Heikkilä, J.: Cell proposal network for microscopy image analysis. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 3199–3203. IEEE (2016)

    Google Scholar 

  29. Arteta, C., Lempitsky, V., Noble, J.A., Zisserman, A.: Learning to detect cells using non-overlapping extremal regions. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 348–356. Springer (2012)

    Google Scholar 

  30. Magnusson, K.E., Jaldén, J.: A batch algorithm using iterative application of the Viterbi algorithm to track cells and construct cell lineages. In: 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI), pp. 382–385. IEEE (2012)

    Google Scholar 

  31. Kainz, P., Urschler, M., Schulter, S., Wohlhart, P., Lepetit, V.: You should use regression to detect cells. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 276–283. Springer (2015)

    Google Scholar 

  32. Maška, M., Ulman, V., Svoboda, D., Matula, P., Matula, P., Ederra, C., Urbiola, A., España, T., Venkatesan, S., Balak, D.M.: A benchmark for comparison of cell tracking algorithms. Bioinformatics 30(11), 1609–1617 (2014)

    Article  Google Scholar 

  33. Roth, H.R., Lu, L., Seff, A., Cherry, K.M., Hoffman, J., Wang, S., Liu, J., Turkbey, E., Summers, R.M.: A new 2.5D representation for lymph node detection using random sets of deep convolutional neural network observations. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 520–527. Springer (2014)

    Google Scholar 

  34. Ghafoorian, M., Platel, B.: Convolutional neural networks for MS lesion segmentation, method description of DIAG team. In: Proceedings of the 2015 Longitudinal Multiple Sclerosis Lesion Segmentation Challenge, pp. 1–2 (2015)

    Google Scholar 

  35. Vaidya, S., Chunduru, A., Muthuganapathy, R., Krishnamurthi, G.: Longitudinal multiple sclerosis lesion segmentation using 3D convolutional neural networks. In: Proceedings of the 2015 Longitudinal Multiple Sclerosis Lesion Segmentation Challenge, pp. 1–2 (2015)

    Google Scholar 

  36. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:14091556 (2014)

  37. Xie, Y., Zhang, Z., Sapkota, M., Yang, L.: Spatial clockwork recurrent neural network for muscle perimysium segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 185–193. Springer (2016)

    Google Scholar 

  38. Stollenga, M.F., Byeon, W., Liwicki, M., Schmidhuber, J.: Parallel multi-dimensional LSTM, with application to fast biomedical volumetric image segmentation. In: Advances in Neural Information Processing Systems, pp. 2998–3006 (2015)

    Google Scholar 

  39. Andermatt, S., Pezold, S., Cattin, P.: Multi-dimensional gated recurrent units for the segmentation of biomedical 3D-data. In: International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, pp. 142–151. Springer (2016)

    Google Scholar 

  40. Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:14061078 (2014)

  41. Poudel, R.P., Lamata, P., Montana, G.: Recurrent fully convolutional neural networks for multi-slice MRI cardiac segmentation. In: International Workshop on Reconstruction and Analysis of Moving Body Organs, pp. 83–94. Springer (2016)

    Google Scholar 

  42. Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234–241. Springer (2015)

    Google Scholar 

  43. Shakeri, M., Tsogkas, S., Ferrante, E., Lippe, S., Kadoury, S., Paragios, N., Kokkinos, I.: Sub-cortical brain structure segmentation using F-CNN’s. In: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), pp. 269–272. IEEE (2016)

    Google Scholar 

  44. Song, Y., Zhang, L., Chen, S., Ni, D., Lei, B., Wang, T.: Accurate segmentation of cervical cytoplasm and nuclei based on multiscale convolutional network and graph partitioning. IEEE Trans. Biomed. Eng. 62(10), 2421–2433 (2015)

    Article  Google Scholar 

  45. Alansary, A., Kamnitsas, K., Davidson, A., Khlebnikov, R., Rajchl, M., Malamateniou, C., Rutherford, M., Hajnal, J.V., Glocker, B., Rueckert, D.: Fast fully automatic segmentation of the human placenta from motion corrupted MRI. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 589–597. Springer (2016)

    Google Scholar 

  46. Cai, J., Lu, L., Zhang, Z., Xing, F., Yang, L., Yin, Q.: Pancreas segmentation in MRI using graph-based decision fusion on convolutional neural networks. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 442–450. Springer (2016)

    Google Scholar 

  47. Christ, P.F., Elshaer, M.E.A., Ettlinger, F., Tatavarty, S., Bickel, M., Bilic, P., Rempfler, M., Armbruster, M., Hofmann, F., D’Anastasi, M.: Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 415–423. Springer (2016)

    Google Scholar 

  48. Dou, Q., Chen, H., Yu, L., Zhao, L., Qin, J., Wang, D., Mok, V.C., Shi, L., Heng, P.-A.: Automatic detection of cerebral microbleeds from MR images via 3D convolutional neural networks. IEEE Trans. Med. Imaging 35(5), 1182–1195 (2016)

    Article  Google Scholar 

  49. Fu, H., Xu, Y., Lin, S., Wong, D.W.K., Liu, J.: Deepvessel: retinal vessel segmentation via deep learning and conditional random field. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 132–139. Springer (2016)

    Google Scholar 

  50. Gao, M., Xu, Z., Lu, L., Wu, A., Nogues, I., Summers, R.M., Mollura, D.J.: Segmentation label propagation using deep convolutional neural networks and dense conditional random field. In: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), pp. 1265–1268. IEEE (2016)

    Google Scholar 

  51. Shin, H.-C., Roth, H.R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., Summers, R.M.: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016)

    Article  Google Scholar 

  52. Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? In: Advances in Neural Information Processing Systems, pp. 3320–3328 (2014)

    Google Scholar 

  53. Ravishankar, H., Sudhakar, P., Venkataramani, R., Thiruvenkadam, S., Annangi, P., Babu, N., Vaidya, V.: Understanding the mechanisms of deep transfer learning for medical images. In: International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, pp. 188–196. Springer (2016)

    Google Scholar 

  54. Akselrod-Ballin, A., Karlinsky, L., Alpert, S., Hasoul, S., Ben-Ari, R., Barkan, E.: A region based convolutional network for tumor detection and classification in breast mammography. In: International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, pp. 197–205. Springer (2016)

    Google Scholar 

  55. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)

    Google Scholar 

  56. Smistad, E., Løvstakken, L.: Vessel detection in ultrasound images using deep convolutional neural networks. In: International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, pp. 30–38. Springer (2016)

    Google Scholar 

  57. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)

    Google Scholar 

  58. Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: Convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 675–678. ACM (2014)

    Google Scholar 

  59. Smistad, E., Bozorgi, M., Lindseth, F.: FAST: framework for heterogeneous medical image computing and visualization. Int. J. Comput. Assist. Radiol. Surg. 10(11), 1811–1822 (2015)

    Article  Google Scholar 

  60. Dey, D., Chaudhuri, S., Munshi, S.: Obstructive sleep apnoea detection using convolutional neural network based deep learning framework. Biomed. Eng. Lett. 8(1), 95–100 (2018)

    Article  Google Scholar 

  61. Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2016)

    Article  Google Scholar 

  62. Kulkarni, K., Lohit, S., Turaga, P., Kerviche, R., Ashok, A.: Reconnet: non-iterative reconstruction of images from compressively sensed measurements. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 449–458 (2016)

    Google Scholar 

  63. Janowczyk, A., Basavanhally, A., Madabhushi, A.: Stain normalization using sparse autoencoders (StaNoSA): application to digital pathology. Comput. Med. Imaging Graph. 57, 50–61 (2017)

    Article  Google Scholar 

  64. Benou, A., Veksler, R., Friedman, A., Raviv, T.R.: De-noising of contrast-enhanced MRI sequences by an ensemble of expert deep neural networks. In: International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, pp. 95–110. Springer (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Institute of Technology, The University of Kashmir, Srinagar, 190006, J&K, India

    Abdul Mueed Hafiz & Ghulam Mohiuddin Bhat

Authors
  1. Abdul Mueed Hafiz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ghulam Mohiuddin Bhat
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abdul Mueed Hafiz .

Editor information

Editors and Affiliations

  1. Singidunum University, Belgrade, Serbia

    Milan Tuba

  2. Department of Electronics and Communication Engineering, ITM University, Gwalior, Madhya Pradesh, India

    Shyam Akashe

  3. Sabar Institute of Technology, Gujarat Technological University, Ahmedabad, Gujarat, India

    Amit Joshi

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Hafiz, A.M., Bhat, G.M. (2020). A Survey of Deep Learning Techniques for Medical Diagnosis. In: Tuba, M., Akashe, S., Joshi, A. (eds) Information and Communication Technology for Sustainable Development. Advances in Intelligent Systems and Computing, vol 933. Springer, Singapore. https://doi.org/10.1007/978-981-13-7166-0_16

Download citation

Publish with us

Policies and ethics

Search

Navigation