Advertisement

Medical Text and Image Processing: Applications, Issues and Challenges

  • Shweta AgrawalEmail author
  • Sanjiv Kumar Jain
Chapter
  • 20 Downloads
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 13)

Abstract

Text and image analysis are playing very important role in healthcare and medical domain. The whole clinical process is getting affected positively by text and image processing. Many datasets, algorithms, models and tools are available for extracting useful information and for applying natural language processing, machine learning and deep learning algorithms. But there exist many challenges in healthcare data for successful implementation of text and image based machine learning models, which include: (i) storage and retrieval of high resolution images, (ii) scarcity of data (iii) dataset generation and validation, (iv) appropriate algorithms and models for extracting hidden information from images and texts, (v) use of modern concepts like deep neural networks, recurrent neural network, (vi) data wrangling and (vii) processing capacity of processors. This chapter will: (i) establish a background for the research work in the area of machine learning and deep learning, (ii) provide the brief about various types of medical texts and images, (iii) discuss the various existing models and applications of natural language processing, machine learning, computer vision and deep learning in medical domain and (iv) discuss various issues and challenges on applying natural language processing, machine learning and deep learning on medical data.

Keywords

Machine learning Deep learning Medical imaging Information extraction Medical text Electronic health records 

References

  1. 1.
    S.E. Dilsizian, E.L. Siegel, Artificial intelligence in medicine and cardiac imaging: harnessing big data and advanced computing to provide personalized medical diagnosis and treatment. Curr. Cardiol. Rep. 16(1), 441–448 (2014)CrossRefGoogle Scholar
  2. 2.
    F. Jiang, Y. Jiang, H. Zhi, Y. Dong, H. Li, S. Ma, Y. Wang, Q. Dong, H. Shen, Y. Wang, Artificial intelligence in healthcare: past, present and future. Stroke Vasc. Neurol. 2(4), 230–243 (2017)CrossRefGoogle Scholar
  3. 3.
    J. He, S.L. Baxter, J. Xu, J. Xu, X. Zhou, K. Zhang, The practical implementation of artificial intelligence technologies in medicine. Nat. Med. 25(1), 30–36 (2019)CrossRefGoogle Scholar
  4. 4.
    G. Quer, E.D. Muse, N. Nikzad, E.J. Topol, S.R. Steinhubl, Augmenting diagnostic vision with AI. Lancet 390(10091), 221 (2017)CrossRefGoogle Scholar
  5. 5.
    A. Esteva, A. Robicquet, B. Ramsundar, V. Kuleshov, M. DePristo, K. Chou, C. Cui, G. Corrado, S. Thrun, J. Dean, A guide to deep learning in healthcare. Nat. Med. 25(1), 24–29 (2019)CrossRefGoogle Scholar
  6. 6.
    T. Ching, D.S. Himmelstein, B.K. Beaulieu-Jones, A.A. Kalinin, B.T. Do, G.P. Way, E. Ferrero, P.M. Agapow, M. Zietz, M.M. Hoffman et al., Opportunities and obstacles for deep learning in biology and medicine. J. R. Soc. Interface 15(141), 20170387 (2018)CrossRefGoogle Scholar
  7. 7.
    D. Rav`i, C. Wong, F. Deligianni, M. Berthelot, J. Andreu-Perez, B. Lo, G.Z. Yang, Deep learning for health informatics. IEEE J. Biomed. Health Inform. 21(1), 4–21 (2017)CrossRefGoogle Scholar
  8. 8.
    J. Luo, M. Wu, D. Gopukumar, Y. Zhao, Big data application in biomedical research and health care: a literature review. Biomed. Inform. Insights 8:BII–S31559 (2016)Google Scholar
  9. 9.
    P.B. Jensen, L.J. Jensen, S. Brunak, Mining electronic health records: towards better research applications and clinical care. Nat. Rev. Genet. 13(6), 395–405 (2012)CrossRefGoogle Scholar
  10. 10.
    M.L. Littman, Reinforcement learning improves behaviour from evaluative feedback. Nature 521(7553), 445–451 (2015)CrossRefGoogle Scholar
  11. 11.
    O. Gottesman, F. Johansson, M. Komorowski, A. Faisal, D. Sontag, F. Doshi-Velez, L.A. Celi, Guidelines for reinforcement learning in healthcare. Nat. Med. 25(1), 16–18 (2019)CrossRefGoogle Scholar
  12. 12.
    Y. Bengio, A. Courville, P. Vincent, Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013)CrossRefGoogle Scholar
  13. 13.
    Y. LeCun, Y. Bengio, G. Hinton, Deep learning. Nature 521(7553), 436–444 (2015)CrossRefGoogle Scholar
  14. 14.
    N. Ganapathy, R. Swaminathan, T.M. Deserno, Deep learning on 1-D biosignals: a taxonomy-based survey. Yearb. Med. Inform. 27(01), 098–109 (2018)CrossRefGoogle Scholar
  15. 15.
    S. Sonoda, N. Murata, Neural network with unbounded activation functions is universal approximator. Appl. Comput. Harmonic Anal. 43(2), 233–268 (2017)MathSciNetzbMATHCrossRefGoogle Scholar
  16. 16.
    J.T. Springenberg, A. Dosovitskiy, T. Brox, M. Riedmiller, Striving for simplicity: the all convolutional net. arXiv preprint arXiv 1412(6806) (2014)Google Scholar
  17. 17.
    N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, R. Salakhutdinov, Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)MathSciNetzbMATHGoogle Scholar
  18. 18.
    S. Ioffe C. Szegedy, Batch normalization: accelerating deep network training by reducing internal covariate shift, in International Conference on Machine Learning vol. 1502, no. 03167 (2015), pp. 448–456Google Scholar
  19. 19.
    A. Graves, M. Liwicki, S. Fernandez, R. Bertolami, H. Bunke, J. Schmidhuber, A novel connectionist system for improved unconstrained handwriting recognition. IEEE Trans. Pattern Anal. Mach. Intell. 31(5), 855–868 (2009)CrossRefGoogle Scholar
  20. 20.
    S. Hasim, S. Andrew, B. Francoise, Long short-term memory recurrent neural network architectures for large scale acoustic modeling. In: Fifteenth Annual Conference of the International Speech Communication Association (2014)Google Scholar
  21. 21.
    L. Xiangang, W. Xihong, Constructing long short-term memory based deep recurrent neural networks for large vocabulary speech recognition (2014). ArXiv 1410(4281)Google Scholar
  22. 22.
    R. Flynn, T.M. Macdonald, N. Schembri, G.D. Murray, A.S.F. Doney, Automated data capture from free-text radiology reports to enhance accuracy of hospital inpatient stroke codes. Pharmacoepidemiol. Drug Saf. 19(8), 843–847 (2010)CrossRefGoogle Scholar
  23. 23.
    L.L. Popejoy, M.A. Khalilia, M. Popescu, C. Galambos, V. Lyons, M. Rantz et al., Quantifying care coordination using natural language processing and domain-specific ontology. J. Am. Med. Inform. Assoc. 22(e1), e93–e103 (2015)Google Scholar
  24. 24.
    H. Yang, I. Spasic, J.A. Keane, G. Nenadic, A text mining approach to the prediction of disease status from clinical discharge summaries. J. Am. Med. Inform. Assoc. 16(4), 596–600 (2009)CrossRefGoogle Scholar
  25. 25.
    J. Gao, Y. Yang, P. Lin, D.S. Park, Computer vision in healthcare applications. J. Healthc. Eng. 10, 5157020 (2018)Google Scholar
  26. 26.
    Medical Imaging and Technology Alliance (2018) Medical Imaging Modalities, MITA, Arlington, VA. Accessed 12 Oct 2019Google Scholar
  27. 27.
    V.L. Clark, J.A. Kruse, Clinical methods: the history, physical, and laboratory examinations. JAMA 264(21), 2808–2809 (1990)CrossRefGoogle Scholar
  28. 28.
    I. Gur, D. Gur, J.A. Recabaren, The computerized synoptic operative report a novel tool in surgical residency education. Arch. Surg. 147(1), 71–74 (2012)CrossRefGoogle Scholar
  29. 29.
    Joint Commission on the Accreditation of Healthcare Organizations. Standard IM.6.10, EP 7 Website. http://www.jointcommission.org/NR/rdonlyres/A9E4F954-F6B5-4B2D-9ECF-C1E792BF390A/0/D_CurrenttoRevised_DC_HAP.pdf. Accessed 31 Aug 2019
  30. 30.
    D. Volkland, R.L. Iles, Guidebook to Better Medical Writing (Island Press, Washington, DC, 1997)Google Scholar
  31. 31.
    R.A. Rison, A guide to writing case reports. J. Med. Case Rep. 7, 239 (2013)CrossRefGoogle Scholar
  32. 32.
    Y. Luo, P. Szolovits, A.S. Dighe, J.M. Baron, Using machine learning to predict laboratory test results. Am. J. Clin. Pathol. 145(6), 778–788 (2016)CrossRefGoogle Scholar
  33. 33.
    NIH Clinic Center (2017) Chest X-ray images, meta data and diagnosis. https://nihcc.app.box.com/v/ChestXray-NIHCC. Accessed 31August 2019
  34. 34.
    The Cancer Imaging, Archive Medical images of cancer like PET/CT (2019). https://www.cancerimagingarchive.net. Accessed 3 Sept 2019
  35. 35.
    National Biomedical Imaging Archive, Image for development and validation of analytical software tools (2018). https://imaging.nci.nih.gov/ncia/login.jsf. Accessed 31 Aug 2019
  36. 36.
    The Open Access Series of Imaging Studies, Neuroimaging data sets of the brain (2010). http://www.oasis-brains.org/. Accessed 31 Aug 2019
  37. 37.
    The Federal Interagency Traumatic Brain Injury Research, MRI, PET, Contrast, and other data on a range of Traumatic brain injury (TBI) (2012). https://fitbir.nih.gov/. Accessed 31 Aug 2019
  38. 38.
    Clemson University, Structured Analysis of the Retina (STARE) (1975). http://cecas.clemson.edu/~ahoover/stare/. Accessed 1 Sept 2019
  39. 39.
    Alzheimer’s Disease Neuroimaging, MRI and PET images, genetics, cognitive tests, CSF and blood biomarkers as predictors of the Alzheimer’s disease Initiative (2014). http://adni.loni.usc.edu/. Accessed 1 Sept 2019
  40. 40.
    The Center for In Vivo Microscopy, Duke University, Medical Center Highest resolution images of MRI, CT, X-Ray, ultrasound, confocal, optical, and SPECT (2013). http://www.civm.duhs.duke.edu/SharedData/DataSupplements.htm. Accessed 1 Sept 2019
  41. 41.
    X. Luke, A.T. Layton, N. Wang, P.E.Z. Larson, J.L. Zhang, V.S. Lee, C. Liu, G.A. Johnson, Dynamic contrast-enhanced quantitative susceptibility mapping with ultrashort echo time MRI for evaluating renal function. Am. J. Physiol. Renal Physiol. 310(2), F174–F182 (2015)Google Scholar
  42. 42.
    Midas Platform: Open-Source Toolkit, Flexible, intelligent data storage system (2010). https://www.insight-journal.org/midas/. Accessed 2 Sept 2019
  43. 43.
    Midas Platform: Open-Source Toolkit, National Alliance for Medical Image Computing (NAMIC) (2010). https://www.insight-journal.org/midas/community/view/17. Accessed 2 Sept 2019
  44. 44.
    Midas Platform: Open-Source Toolkit, Imaging Methods Assessment and Reporting (IMAR) (2010). https://www.insight-journal.org/midas/community/view/15. Accessed 2 Sept 2019
  45. 45.
    Digital Retinal Images for Vessel Extraction, Database for comparative studies on segmentation of blood vessels in retinal images (2012). https://drive.grand-challenge.org/. Accessed 3 Sept 2019
  46. 46.
    Digital Database for Screening Mammography, Mammographic images (2006). http://www.eng.usf.edu/cvprg/Mammography/Database.html. Accessed 3 Sept 2019
  47. 47.
    Public Lung Database to Address Drug Response, A public image database to support research in computer aided diagnosis (2009). http://www.via.cornell.edu/crpf.html. Accessed 3 Sept 2019
  48. 48.
    A.P. Reeves, A.M. Biancardi, D. Yankelevitz, S. Fotin, B.M. Keller, A. Jirapatnakul, J. Lee, A public image database to support research in computer aided diagnosis, in 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (2009), pp. 3715–3718Google Scholar
  49. 49.
    The Osteoarthritis Initiative, Data related to prevention and treat knee osteoarthritis (2013). https://oai.epi-ucsf.org/datarelease/. Accessed 3 Sept 2019
  50. 50.
    Image Sciences Institute, University Medical Center Utrecht, SCR database: Segmentation in Chest Radiographs (2018). http://www.isi.uu.nl/Research/Databases/SCR/. Accessed 3 Sept 2019
  51. 51.
    Japanese Society of Radiological Technology (2004) Digital Image Database. http://db.jsrt.or.jp/eng.php. Accessed 4 Sept 2019
  52. 52.
    J. Shiraishi, S. Katsuragawa, J. Ikezoe, T. Matsumoto, T. Kobayashi, K. Komatsu, M. Matsui, H. Fujita, Y. Kodera, K. Doi, Development of a digital image database for chest radiographs with and without a lung nodule: Receiver operating characteristic analysis of radiologists’ detection of pulmonary nodules. Am. J. Roentgenol. 174(1), 1–74 (2000)CrossRefGoogle Scholar
  53. 53.
    LUT School of Business and Management, Standard Diabetic Retinopathy Database Calibration level 1 (2007). http://www2.it.lut.fi/project/imageret/diaretdb1/. Accessed 4 Sept 2019
  54. 54.
    T. Kauppi, V. Kalesnykiene, J.K. Kamarainen, L. Lensu, I. Sorri, A. Raninen, R. Voutilainen, H. Uusitalo, H. Kälviäinen, J. Pietilä, DIARETDB1 diabetic retinopathy database and evaluation protocol, in Proceedings of the 11th Conference on Medical Image Understanding and Analysis (Aberystwyth, Wales, 2007)Google Scholar
  55. 55.
    Cornell Visualization and Image Analysis Group, ECLAP public database of whole lung CT images (2019). http://www.via.cornell.edu/databases/. Accessed 4 Sept 2019
  56. 56.
    Omni Medical Search, Medical image and study databases (2008). http://www.omnimedicalsearch.com/image_databases.html. Accessed 4 Sept 2019
  57. 57.
    SpineWeb, A platform for getting spinal images for image analysis (2014). http://spineweb.digitalimaginggroup.ca/. Accessed 5 Sept 2019
  58. 58.
    Facebase, Repository for datasets by organisms, experiment type, age stage, mutation, genotype and more (2019). https://www.facebase.org/chaise/recordset/#1/isa:dataset. Accessed 5 Sept 2019
  59. 59.
    CLAMP, Natural Language Processing Software (2018). https://clamp.uth.edu/. Accessed 16 Oct 2019
  60. 60.
    A.R. Aronson, Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program, in Proceedings of the AMIA Symposium (2001), pp. 17–21Google Scholar
  61. 61.
    A.R. Aronson, F.M. Lang, An overview of MetaMap: historical perspective and recent advances. J. Am. Med. Inform. Assoc. 17, 229–236 (2010)CrossRefGoogle Scholar
  62. 62.
    A Medical Language Extraction and Encoding System http://www.medlingmap.org/taxonomy/term/80. Accessed 28 Oct 2019
  63. 63.
    C. Friedman, P.O. Alderson, J.H. Austin, J.J. Cimino, S.B. Johnson, A general natural-language text processor for clinical radiology. J. Am. Med. Inform. Assoc. JAMIA 1, 161–174 (1994)CrossRefGoogle Scholar
  64. 64.
    H. Xu, Z. Fu, A. Shah et al. Extracting and integrating data from entire electronic health records for detecting colorectal cancer cases, in Proceedings of AMIA Symposium (2011), pp. 1564–1572Google Scholar
  65. 65.
    J.H. Chiang, J.W. Lin, C.W. Yang, Automated evaluation of electronic discharge notes to assess quality of care for cardiovascular diseases using Medical Language Extraction and Encoding System (MedLEE). J. Am. Med. Inform. Assoc. JAMIA 17, 245–252 (2010)CrossRefGoogle Scholar
  66. 66.
    G.K. Savova, J. Fan, Z. Ye, S.P. Murphy, J. Zheng, C.G. Chute, I.J. Kullo, Discovering peripheral arterial disease cases from radiology notes using natural language processing, in AMIA Annual Symposium Proceedings (2010), pp. 722–726Google Scholar
  67. 67.
    T. Groza, S. Köhler, S. Doelken, N. Collier, A. Oellrich, D. Smedley et al., Automatic concept recognition using the human phenotype ontology reference and test suite corpora. Database 2015 (2015)Google Scholar
  68. 68.
    M. Lobo, A. Lamurias, F.M. Couto, Identifying human phenotype terms by combining machine learning and validation rules. BioMed Res. Int. 2017 (2017)Google Scholar
  69. 69.
    M.J. Sobrido Gómez, M. Pardo Pérez,. Automated semantic annotation of rare disease cases: a case study. Database J. Biol. Databases Curation (bau045) (2014)Google Scholar
  70. 70.
    J.B. Hawkins, J.S. Brownstein, G. Tuli, T. Runels, K. Broecker, E.O. Nsoesie, D.J. McIver, R. Rozenblum, A. Wright, F.T. Bourgeois, F. Greaves, Measuring patient-perceived quality of care in US hospitals using Twitter. BMJ Qual, Saf. 25(6), 404–413 (2016)CrossRefGoogle Scholar
  71. 71.
    Automated clinical record keeping. https://trykiroku.com/. Accessed 25 Oct 2019
  72. 72.
    Clinical documentation for iPhone. http://mdops.com/. Accessed 27 Oct 2019
  73. 73.
    D. Demner-Fushman, W.W. Chapman, C.J. McDonald, What can natural language processing do for clinical decision support. J. Biomed. Inform. 42, 760–772 (2009)CrossRefGoogle Scholar
  74. 74.
    V.M. Pai, M. Rodgers, R. Conroy, J. Luo, R. Zhou, B. Seto, Workshop on using natural language processing applications for enhancing clinical decision making: an executive summary. J. Am. Med. Inform. Assoc. 21, e2–e5 (2014)CrossRefGoogle Scholar
  75. 75.
    S. Pradhan, N. Elhadad, B.R. South, D. Martinez, L. Christensen, A. Vogel, H. Suominen, W.W. Chapman, G. Savova, Evaluating the state of the art in disorder recognition and normalization of the clinical narrative. J. Am. Med. Inform. Assoc. 22, 143–154 (2014)CrossRefGoogle Scholar
  76. 76.
    W.W. Chapman, M. Fiszman, J.N. Dowling, B.E. Chapman, T.C. Rindflesch, Identifying respiratory findings in emergency department reports for bio surveillance using MetaMap. Stud. Health Technol. Inform. 107, 487–491 (2004)Google Scholar
  77. 77.
    L. Cui, S.S. Sahoo, S.D. Lhatoo, G. Garg, P. Rai, A. Bozorgi et al., Complex epilepsy phenotype extraction from narrative clinical discharge summaries. J. Biomed. Inform. 51, 272–279 (2014)CrossRefGoogle Scholar
  78. 78.
    C. Shivade, P. Raghavan, E. Fosler-Lussier, P.J. Embi, N. Elhadad, S.B. Johnson et al., A review of approaches to identifying patient phenotype cohorts using electronic health records. J. Am. Med. Inform. Assoc. 21, 221–230 (2014)CrossRefGoogle Scholar
  79. 79.
    S. Baker, A. Korhonen, S. Pyysalo, Cancer hallmark text classification using convolutional neural networks, in The Workshop on Building and Evaluating Resources for Biomedical Text Mining (2016), pp. 1–10Google Scholar
  80. 80.
    M. Asada, M. Miwa, Y. Sasaki, Extracting drug-drug interactions with attention CNNs, in BioNLP (2017), pp. 9–18Google Scholar
  81. 81.
    S. Mohan, N. Fiorini, S. Kim, Z. Lu, Deep learning for biomedical information retrieval: learning textual relevance from click logs, in BioNLP (2017), pp. 222–231Google Scholar
  82. 82.
    L. Sulieman, D. Gilmore, C. French, R.M. Cronin, G.P. Jackson, M. Russell, D. Fabbri, Classifying patient portal messages using Convolutional Neural Networks. J. Biomed. Inform. 74, 59–70 (2017)CrossRefGoogle Scholar
  83. 83.
    Y.H. Lee, Efficiency improvement in a busy radiology practice: determination of musculoskeletal magnetic resonance imaging protocol using deep-learning convolutional neural networks. J. Digit. Imaging 31(5), 604–610 (2018)CrossRefGoogle Scholar
  84. 84.
    W. Salloum, G. Finley, E. Edwards, M. Miller, D. Suendermann-Oeft, Deep learning for punctuation restoration in medical reports, in BioNLP (2017), pp. 159–164Google Scholar
  85. 85.
    H. He, K. Ganjam, N. Jain, J. Lundin, R. White, J. Lin, An insight extraction system on biomedical literature with deep neural networks, in Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (2017), pp. 2691–2701Google Scholar
  86. 86.
    R. Chalapathy, E.Z. Borzeshi, M. Piccardi, Bidirectional LSTM-CRF for clinical concept extraction (2016). arXiv 7–12 (2016)Google Scholar
  87. 87.
    S.M. Shortreed, E. Laber, D.J. Lizotte, T.S. Stroup, J. Pineau, S.A. Murphy, Informing sequential clinical decision-making through reinforcement learning: an empirical study. Mach. Learn. 84(1–2), 109–136 (2011)MathSciNetCrossRefGoogle Scholar
  88. 88.
    T. Pham, T. Tran, D. Phung, S. Venkatesh, Deepcare: a deep dynamic memory model for predictive medicine, in Pacific-Asia Conference on Knowledge Discovery and Data Mining (Springer, Cham, 2016), pp. 30–41Google Scholar
  89. 89.
    E. Choi, M.T. Bahadori, J. Sun, J. Kulas, A. Schuetz, W. Stewart, Retain: an interpretable predictive model for healthcare using reverse time attention mechanism, in Advances in Neural Information Processing Systems (2016), pp. 3504–3512Google Scholar
  90. 90.
    C. Pettit et al., Developing a multi-scale visualization toolkit for use in climate change response. Landscape Ecol. 2012 (2012)Google Scholar
  91. 91.
    Y. Liu, A. Kot, F. Drakopoulos, C. Yao, A. Fedorov, A. Enquobahrie et al., An ITK implementation of a physics-based non-rigid registration method for brain deformation in image-guided neurosurgery. Front. Neuroinform. 8(33) (2014)Google Scholar
  92. 92.
    I. Larrabid, P. Omedas, Y. Martelli, X. Planes, M. Nieber, J. Moya et al., GIMIAS: an open source framework for efficient development of research tools and clinical prototypes, in International Conference on Functional Imaging and Modeling of the Heart (Springer, Berlin, 2009), pp. 417–426Google Scholar
  93. 93.
    A. Esteva, B. Kuprel, R.A. Novoa, J. Ko, S.M. Swetter, H.M. Blau, S. Thrun, Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115–118 (2017)CrossRefGoogle Scholar
  94. 94.
    A. Esteva, B. Kuprel, R.A. Novoa, J. Ko, S.M. Swetter, H.M. Blau et al., Dermatologist-level classification of skin cancer with deep neural net-works. Nature 542, 115–118 (2017)CrossRefGoogle Scholar
  95. 95.
    R. Poplin, A.V. Varadarajan, K. Blumer, Y. Liu, M.V. McConnell, G.S. Corrado et al., Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat. Biomed. Eng. 2(3), 158 (2018)CrossRefGoogle Scholar
  96. 96.
    S. Liu, W. Cai, S. Pujol, R. Kikinis, D. Feng, Early diagnosis of Alzheimer’s disease with deep learning, in 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI) (2014), pp. 1015–1018Google Scholar
  97. 97.
    A. Prasoon, K. Petersen, C. Igel, F. Lauze, E. Dam, M. Nielsen, Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network, in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, Berlin, 2013), pp. 246–253Google Scholar
  98. 98.
    Y. Yoo, T. Brosch, A. Traboulsee, D.K. Li, R. Tam, Deep learning of image features from unlabeled data for multiple sclerosis lesion segmentation, in International Workshop on Machine Learning in Medical Imaging (Springer, Cham, 2014), pp. 117–124Google Scholar
  99. 99.
    J.Z. Cheng, D. Ni, Y.H. Chou, J. Qin, C.M. Tiu, Y.C. Chang et al., Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans. Sci. Rep. 6, 24454 (2016)CrossRefGoogle Scholar
  100. 100.
    V. Gulshan, L. Peng, M. Coram, M.C. Stumpe, D. Wu, Narayanaswamy et al., Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22), 2402–2410 (2016)CrossRefGoogle Scholar
  101. 101.
    Y. Cheng, F. Wang, P. Zhang, J. Hu, Risk prediction with electronic health records: a deep learning approach, in Proceedings of the 2016 SIAM International Conference on Data Mining (2016), pp. 432–440Google Scholar
  102. 102.
    F. Sahba, H.R. Tizhoosh, M.M. Salama, A reinforcement learning framework for medical image segmentation, in The 2006 IEEE International Joint Conference on Neural Network Proceedings (2006), pp. 511–517Google Scholar
  103. 103.
    F. Sahba, H.R. Tizhoosh, M.M. Salama, Application of opposition-based reinforcement learning in image segmentation, in 2007 IEEE Symposium on Computational Intelligence in Image and Signal Processing (2007), pp. 246–251Google Scholar
  104. 104.
    D. Liu, T. Jiang, Deep reinforcement learning for surgical gesture segmentation and classification, in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, Cham, 2018), pp. 247–255Google Scholar
  105. 105.
    A. Alansary, O. Oktay, Y. Li, L. Le Folgoc, B. Hou, G. Vaillant et al., Evaluating reinforcement learning agents for anatomical landmark detection. Med. Image Anal. 53, 156–164 (2019)CrossRefGoogle Scholar
  106. 106.
    S.R. Soroushmeh, K. Najarian, Transforming big data into computational models for personalized medicine and health care. Dialogues Clin. Neurosci. 18(3), 339–343 (2016)Google Scholar
  107. 107.
    F. Cabitza, R. Rasoini, G.F. Gensini, Unintended consequences of machine learning in medicine. JAMA 318(6), 517–518 (2017)CrossRefGoogle Scholar
  108. 108.
    R.B. Correia, L. Li, L.M. Rocha, Monitoring potential drug interactions and reactions via network analysis of instagram user timelines, in Biocomputing 2016: Proceedings of the Pacific Symposium (2016), pp. 492–503Google Scholar
  109. 109.
    A. Nikfarjam, A. Sarker, K. O’Connor, R. Ginn, G. Gonzalez, Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features. J. Am. Med. Inform. Assoc. 22(3), 671–681 (2015)Google Scholar
  110. 110.
    M. Abadi, A. Chu, I. Goodfellow, H.B. McMahan, I. Mironov, K. Talwar, L. Zhang, Deep learning with differential privacy, in Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (2016), pp. 308–318Google Scholar
  111. 111.
    R. Shokri, V. Shmatikov, Privacy-preserving deep learning, in Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security (2015), pp. 1310–1321Google Scholar
  112. 112.
    F. Tramèr, F. Zhang, A. Juels, M.K. Reiter, T. Ristenpart, Stealing machine learning models via prediction apis, in 25th USENIX Security Symposium (2016), pp. 601–618Google Scholar
  113. 113.
    K. Chaudhuri, C. Monteleoni, A.D. Sarwate, Differentially private empirical risk minimization. J. Mach. Learn. Res. 12, 1069–1109 (2011)MathSciNetzbMATHGoogle Scholar
  114. 114.
    R. Gilad-Bachrach, N. Dowlin, K. Laine, K. Lauter, M. Naehrig, J. Wernsing, Cryptonets: applying neural networks to encrypted data with high throughput and accuracy, in International Conference on Machine Learning (2016), pp. 201–210Google Scholar
  115. 115.
    A.C. Yao, Protocols for secure computations, in 23rd Annual Symposium on Foundations of Computer Science (1982), pp. 160–164Google Scholar
  116. 116.
    D.E. Oliver, Y. Shahar, E.H. Shortliffe, M.A. Musen, Representation of change in controlled medical terminologies. Artif. Intell. Med. 15(1), 53–76 (1999)CrossRefGoogle Scholar
  117. 117.
    K. Majumdar, Human scalp EEG processing: various soft computing approaches. Appl. Soft Comput. 11(8), 4433–4447 (2011)CrossRefGoogle Scholar
  118. 118.
    T. Balli, R. Palaniappan, Classification of biological signals using linear and nonlinear features. Physiol. Meas. 31(7), 903 (2010)CrossRefGoogle Scholar
  119. 119.
    P.S. La Rosa, A. Nehorai, H. Eswaran, C.L. Lowery, H. Preissl, Detection of uterine MMG contractions using a multiple change point estimator and the K-means cluster algorithm. IEEE Trans. Biomed. Eng. 55(2), 453–467 (2008)CrossRefGoogle Scholar
  120. 120.
    M. Sun, F. Tang, J. Yi, F. Wang, J. Zhou, Identify susceptible locations in medical records via adversarial attacks on deep predictive models, in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2018), pp. 793–801Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Professor CSE, SIRTSAGE UniversityIndoreIndia
  2. 2.Medi-Caps UniversityIndoreIndia

Personalised recommendations