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Deep Learning Models for Medical Image Analysis: Challenges and Future Directions

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Big Data Analytics (BDA 2019)

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Abstract

Artificial neural network (ANN) introduced in the 1950s, is a machine learning framework inspired by the functioning of human neurons. However, for a long time the ANN remained inadequate in solving real problems, because of - the problems of overfitting and vanishing gradient while training a deep architecture, dearth of computation power, and non-availability of enough data for training the framework. This concept has lately re-emerged, in the form of Deep Learning (DL) which initially developed for computer vision and became immensely popular in several other domains. It gained traction in late 2012, when a DL approach i.e. convolutional neural network won in the ImageNet Classification – an acclaimed worldwide computer vision competition. Thereafter, researchers in practically every domain, including medical imaging, started vigorously contributing in the massively progressing field of DL. The success of DL methods can be owed to the availability of data, boosted computation power provided by the existing graphics processing units (GPUs), and ground-breaking training algorithms. In this paper, we have overviewed the area of DL in medical imaging, including (1) machine learning and DL basics, (2) cause of power of DL, (3) common DL models, (4) their applications to medical imaging and (5) challenges and future work in this field.

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References

  1. Murphy, K.P.: Machine Learning: A Probabilistic Perspective, 1st edn., p. 25. The MIT Press, Cambridge (2012)

    MATH  Google Scholar 

  2. Wang, F., Shen, D., Yan, P., Suzuki, K. (eds.): MLMI 2012. LNCS, vol. 7588. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-35428-1

    Book  Google Scholar 

  3. Shen, D., Wu, G., Zhang, D., Suzuki, K., Wang, F., Yan, P.: Machine learning in medical imaging. Comput. Med. Imaging Graph. 41, 1–2 (2015)

    Article  Google Scholar 

  4. Rana, B., et al.: Graph-theory-based spectral feature selection for computer aided diagnosis of Parkinson’s disease using T 1-weighted MRI. Int. J. Imaging Syst. Technol. 25(3), 245–255 (2015)

    Article  MathSciNet  Google Scholar 

  5. Suzuki, K., Zhou, L., Wang, Q.: Machine learning in medical imaging. Pattern Recognit. 63, 465–467 (2017)

    Article  Google Scholar 

  6. El-Baz, A., Gimel’farb, G., Suzuki, K.: Machine learning applications in medical image analysis. Comput. Math. Methods Med. 2017, 2361061 (2017)

    Article  Google Scholar 

  7. Rana, B., et al.: Relevant 3D local binary pattern based features from fused feature descriptor for differential diagnosis of Parkinson’s disease using structural MRI. Biomed. Signal Process. Control 34, 134–143 (2017)

    Article  Google Scholar 

  8. Juneja, A., Rana, B., Agrawal, R.K.: fMRI based computer aided diagnosis of schizophrenia using fuzzy kernel feature extraction and hybrid feature selection. Multimed. Tools Appl. 77(3), 3963–3989 (2018)

    Article  Google Scholar 

  9. Juneja, A., Rana, B., Agrawal, R.K.: A novel fuzzy rough selection of non-linearly extracted features for schizophrenia diagnosis using fMRI. Comput. Methods Programs Biomed. 155, 139–152 (2018)

    Article  Google Scholar 

  10. Suzuki, K.: Computational Intelligence in Biomedical Imaging. Springer, New York (2014). https://doi.org/10.1007/978-1-4614-7245-2

    Book  Google Scholar 

  11. Shiraishi, J., Li, Q., Suzuki, K., Engelmann, R., Doi, K.: Computer aided diagnostic scheme for the detection of lung nodules on chest radiographs: localized search method based on anatomical classification. Med. Phys. 33(7), 2642–2653 (2006)

    Article  Google Scholar 

  12. Coppini, G., Diciotti, S., Falchini, M., Villari, N., Valli, G.: Neural networks for computer-aided diagnosis: detection of lung nodules in chest radiograms. IEEE Trans. Inf. Technol. Biomed. 7(4), 344–357 (2003)

    Article  Google Scholar 

  13. Hardie, R.C., Rogers, S.K., Wilson, T., Rogers, A.: Performance analysis of a new computer aided detection system for identifying lung nodules on chest radiographs. Med. Image Anal. 12(3), 240–258 (2008)

    Article  Google Scholar 

  14. Chen, S., Suzuki, K., MacMahon, H.: A computer-aided diagnostic scheme for lung nodule detection in chest radiographs by means of two-stage nodule-enhancement with support vector classification. Med. Phys. 38, 1844–1858 (2011)

    Article  Google Scholar 

  15. Arimura, H., Katsuragawa, S., Suzuki, K., et al.: Computerized scheme for automated detection of lung nodules in low-dose computed tomography images for lung cancer screening. Acad. Radiol. 11(6), 617–629 (2004)

    Article  Google Scholar 

  16. Armato III, S.G., Giger, M.L., MacMahon, H.: Automated detection of lung nodules in CT scans: preliminary results. Med. Phys. 28(8), 1552–1561 (2001)

    Article  Google Scholar 

  17. Ye, X., Lin, X., Dehmeshki, J., Slabaugh, G., Beddoe, G.: Shape-based computer-aided detection of lung nodules in thoracic CT images. IEEE Trans. Biomed. Eng. 56(7), 1810–1820 (2009)

    Article  Google Scholar 

  18. Way, T.W., Sahiner, B., Chan, H.P., et al.: Computer-aided diagnosis of pulmonary nodules on CT scans: improvement of classification performance with nodule surface features. Med. Phys. 36(7), 3086–3098 (2009)

    Article  Google Scholar 

  19. Aoyama, M., Li, Q., Katsuragawa, S., MacMahon, H., Doi, K.: Automated computerized scheme for distinction between benign and malignant solitary pulmonary nodules on chest images. Med. Phys. 29(5), 701–708 (2002)

    Article  Google Scholar 

  20. Wu, Y., Doi, K., Giger, M.L., Nishikawa, R.M.: Computerized detection of clustered microcalcifications in digital mammograms: applications of artificial neural networks. Med. Phys. 19(3), 555–560 (1992)

    Article  Google Scholar 

  21. El-Naqa, I., Yang, Y., Wernick, M.N., Galatsanos, N.P., Nishikawa, R.M.: A support vector machine approach for detection of microcalcifications. IEEE Trans. Med. Imaging 21(12), 1552–1563 (2002)

    Article  Google Scholar 

  22. Yu, S.N., Li, K.Y., Huang, Y.K.: Detection of microcalcifications in digital mammograms using wavelet filter and Markov random field model. Comput. Med. Imaging Graph. 30(3), 163–173 (2006)

    Article  Google Scholar 

  23. Ge, J., Sahiner, B., Hadjiiski, L.M., et al.: Computer aided detection of clusters of microcalcifications on full field digital mammograms. Med. Phys. 33(8), 2975–2988 (2006)

    Article  Google Scholar 

  24. Wu, Y.T., Wei, J., Hadjiiski, L.M., et al.: Bilateral analysis based false positive reduction for computer-aided mass detection. Med. Phys. 34(8), 3334–3344 (2007)

    Article  Google Scholar 

  25. Huo, Z., Giger, M.L., Vyborny, C.J., Wolverton, D.E., Schmidt, R.A., Doi, K.: Automated computerized classification of malignant and benign masses on digitized mammograms. Acad. Radiol. 5(3), 155–168 (1998)

    Article  Google Scholar 

  26. Delogu, P., Evelina Fantacci, M., Kasae, P., Retico, A.: Characterization of mammographic masses using a gradient-based segmentation algorithm and a neural classifier. Comput. Biol. Med. 37(10), 1479–1491 (2007)

    Article  Google Scholar 

  27. Shi, J., Sahiner, B., Chan, H.P., et al.: Characterization of mammographic masses based on level set segmentation with new image features and patient information. Med. Phys. 35(1), 280–290 (2008)

    Article  Google Scholar 

  28. Aggarwal, N., Rana, B., Agrawal, R.K.: Role of surfacelet transform in diagnosing Alzheimer’s disease. Multidimens. Syst. Signal Process. 30(4), 1839–1858 (2019)

    Article  MATH  Google Scholar 

  29. Verma, H., Agrawal, R.K., Sharan, A.: An improved intuitionistic fuzzy c-means clustering algorithm incorporating local information for brain image segmentation. Appl. Soft Comput. 46, 543–557 (2016)

    Article  Google Scholar 

  30. Suzuki, K.: Machine learning in computer-aided diagnosis of the thorax and colon in CT: a survey. IEICE Trans. Inf. Syst. E96-D(4), 772–783 (2013)

    Article  Google Scholar 

  31. Suzuki, K.: Machine Learning in Computer-Aided Diagnosis: Medical Imaging Intelligence and Analysis. IGI Global, Hershey (2012)

    Book  Google Scholar 

  32. Byvatov, E., Fechner, U., Sadowski, J., Schneider, G.: Comparison of support vector machine and artificial neural network systems for drug/nondrug classification. J. Chem. Inf. Comput. Sci. 43, 1882–1889 (2003)

    Article  Google Scholar 

  33. Tong, S., Chang, E.: Support vector machine active learning for image retrieval. In: Proceedings of the 9th ACM International Conference on Multimedia, Ottawa, Canada, 5 October–30 September 2001, p. 107. ACM, New York (2001)

    Google Scholar 

  34. Arbib, M.A.: The Handbook of Brain Theory and Neural Networks, 2nd edn. The MIT Press, Boston (2003)

    MATH  Google Scholar 

  35. Haykin, S.S.: Neural Networks: A Comprehensive Foundation, pp. 107–116. Macmillan College Publishing, New York (1994)

    MATH  Google Scholar 

  36. 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 

  37. Basaia, S., et al.: Automated classification of Alzheimer’s disease and mild cognitive impairment using a single MRI and deep neural networks. NeuroImage Clin. 21, 101645 (2019). Alzheimer’s disease neuroimaging initiative

    Article  Google Scholar 

  38. Chen, L., Shi, J., Peng, B., Dai, Y.: Computer-aided diagnosis of Parkinson’s disease based on the stacked deep polynomial networks ensemble learning framework. Sheng wu yi xue gong cheng xue za zhi = J. Biomed. Eng. = Shengwu yixue gongchengxue zazhi 35(6), 928–934 (2018)

    Google Scholar 

  39. Zeng, L.L., et al.: Multi-site diagnostic classification of schizophrenia using discriminant deep learning with functional connectivity MRI. EBioMedicine 30, 74–85 (2018)

    Article  Google Scholar 

  40. Milletari, F., et al.: Hough-CNN: deep learning for segmentation of deep brain regions in MRI and ultrasound. Comput. Vis. Image Understand. 1(164), 92–102 (2017)

    Article  Google Scholar 

  41. Fukushima, K.: Neocognitron: a self organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern. 36(4), 193–202 (1980)

    Article  MATH  Google Scholar 

  42. Suzuki, K.: Overview of deep learning in medical imaging. Radiol. Phys. Technol. 10(3), 257–273 (2017)

    Article  Google Scholar 

  43. Mikolov, T., Karafiát, M., Burget, L., Cernocký, J., Khudanpur, S.: Recurrent neural network based language model. In: Proceedings of the 11th Annual Conference of the International Speech Communication Association (INTERSPEECH 2010), Makuhari, Japan, 26–30 September 2010. International Speech Communication Association, pp. 1045–1048 (2010)

    Google Scholar 

  44. Gregor, K., Danihelka, I., Graves, A., Rezende, D.J., Wierstra, D.: DRAW: a recurrent neural network for image generation. In: Proceedings of the 32nd International Conference on Machine Learning (ICML 2015), Lille, France, 6–11 July 2015, pp. 1462–1471. JMLR (2015)

    Google Scholar 

  45. 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 

  46. Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.A.: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11, 3371–3408 (2010)

    MathSciNet  MATH  Google Scholar 

  47. Bengio, Y., Lamblin, P., Popovici, D., Larochelle, H.: Greedy layer-wise training of deep networks. In: Advances in Neural Information Processing Systems, pp. 153–160 (2007)

    Google Scholar 

  48. Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  49. Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  50. Wang, D., Khosla, A., Gargeya, R., Irshad, H., Beck, A.H.: Deep learning for identifying metastatic breast cancer. arXiv preprint: arXiv:1606.05718, 18 June 2016

  51. Hua, K.L., Hsu, C.H., Hidayati, S.C., Cheng, W.H., Chen, Y.J.: Computer-aided classification of lung nodules on computed tomography images via deep learning technique. OncoTargets Ther. 8, 2015–2022 (2015)

    Google Scholar 

  52. Kumar, D., Wong, A., Clausi, D.A.: Lung nodule classification using deep features in CT images. In: Proceedings of the 2015 12th Conference on Computer and Robot Vision, Halifax, Canada, 3–5 June 2015, pp. 133–138. IEEE (2015)

    Google Scholar 

  53. Suk, H.I., Lee, S.W., Shen, D.: Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis. Neuroimage 101, 569–582 (2014). Alzheimer’s disease neuroimaging initiative

    Article  Google Scholar 

  54. Suk, H.I., Shen, D.: Deep learning-based feature representation for AD/MCI classification. Med. Image Comput. Comput. Assist. Interv. 16(Pt 2), 583–590 (2013)

    Google Scholar 

  55. Liu, S., Lis, S., Cai, W., Pujol, S., Kikinis, R., Feng, D.: Early diagnosis of Alzheimer’s disease with deep learning. In: Proceedings of the IEEE 11th International Symposium on Biodmedical Imaging, Beijing, China, 29 April–2 May 2014, pp. 1015–1018. IEEE (2014)

    Google Scholar 

  56. Cheng, J.Z., Ni, D., Chou, Y.H., Qin, J., Tiu, C.M., Chang, Y.C., 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)

    Article  Google Scholar 

  57. Kallenberg, M., et al.: Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring. IEEE Trans. Med. Imaging 35, 1322–1331 (2016)

    Article  Google Scholar 

  58. Chen, J., Chen, J., Ding, H.Y., Pan, Q.S., Hong, W.D., Xu, G., et al.: Use of an artificial neural network to construct a model of predicting deep fungal infection in lung cancer patients. Asian Pac. J. Cancer Prev. 16, 5095–5099 (2015)

    Article  Google Scholar 

  59. Liu, Y., Wang, J.: PACS and Digital Medicine: Essential Principles and Modern Practice, 1st edn. CRC Press, Boca Raton (2010)

    Book  Google Scholar 

  60. Collins, F.S., Varmus, H.: A new initiative on precision medicine. N. Engl. J. Med. 372, 793–795 (2015)

    Article  Google Scholar 

  61. Aerts, H.J., Velazquez, E.R., Leijenaar, R.T., Parmar, C., Grossmann, P., Carvalho, S., et al.: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat. Commun. 5, 4006 (2014)

    Article  Google Scholar 

  62. Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115 (2017)

    Article  Google Scholar 

  63. Suk, H.-I., Shen, D.: Deep learning-based feature representation for AD/MCI classification. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8150, pp. 583–590. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40763-5_72

    Chapter  Google Scholar 

  64. Suk, H.I., Lee, S.W., Shen, D.: Latent feature representation with stacked auto-encoder for AD/MCI diagnosis. Brain Struct. Funct. 220(2), 841–859 (2015). Alzheimer’s disease neuroimaging initiative

    Article  Google Scholar 

  65. Suk, H.I., Wee, C.Y., Lee, S.W., Shen, D.: State-space model with deep learning for functional dynamics estimation in resting-state fMRI. NeuroImage 129, 292–307 (2016)

    Article  Google Scholar 

  66. Menegola, A., Fornaciali, M., Pires, R., Avila, S., Valle, E.: Towards automated melanoma screening: exploring transfer learning schemes. arXiv preprint: arXiv:1609.01228, 5 September 2016

  67. Hesamian, M.H., Jia, W., He, X., Kennedy, P.: Deep learning techniques for medical image segmentation: achievements and challenges. J. Digit. Imaging 32(4), 582–596 (2019)

    Article  Google Scholar 

  68. Zhang, W., et al.: Deep convolutional neural networks for multi-modality isointense infant brain image segmentation. NeuroImage 108, 214–224 (2015)

    Article  Google Scholar 

  69. Bar, Y., Diamant, I., Wolf, L., Greenspan, H.: Deep learning with non-medical training used for chest pathology identification. In: Medical Imaging 2015: Computer-Aided Diagnosis, vol. 9414, p. 94140V. International Society for Optics and Photonics, 20 March 2015

    Google Scholar 

  70. Bergamo, A., Torresani, L., Fitzgibbon, A.W.: PiCoDes: learning a compact code for novel-category recognition. In: Advances in Neural Information Processing Systems, pp. 2088–2096 (2011)

    Google Scholar 

  71. Moeskops, P., et al.: Deep learning for multi-task medical image segmentation in multiple modalities. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016, Part II. LNCS, vol. 9901, pp. 478–486. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_55

    Chapter  Google Scholar 

  72. Prasoon, A., Petersen, K., Igel, C., Lauze, F., Dam, E., Nielsen, M.: Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part II. LNCS, vol. 8150, pp. 246–253. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40763-5_31

    Chapter  Google Scholar 

  73. Roth, H.R., Lu, L., Farag, A., Sohn, A., Summers, R.M.: Spatial aggregation of holistically-nested networks for automated pancreas segmentation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016, Part II. LNCS, vol. 9901, pp. 451–459. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_52

    Chapter  Google Scholar 

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  1. Jawaharlal Nehru University, New Delhi, 110067, India

    R. K. Agrawal & Akanksha Juneja

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  1. R. K. Agrawal
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  2. Akanksha Juneja
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Correspondence to R. K. Agrawal .

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Editors and Affiliations

  1. Missouri University of Science and Technology, Rolla, MO, USA

    Sanjay Madria

  2. Harbin Institute of Technology, Shenzhen, China

    Philippe Fournier-Viger

  3. Ahmedabad University, Ahmedabad, India

    Sanjay Chaudhary

  4. International Institute of Information Technology, Hyderabad, India

    P. Krishna Reddy

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Agrawal, R.K., Juneja, A. (2019). Deep Learning Models for Medical Image Analysis: Challenges and Future Directions. In: Madria, S., Fournier-Viger, P., Chaudhary, S., Reddy, P. (eds) Big Data Analytics. BDA 2019. Lecture Notes in Computer Science(), vol 11932. Springer, Cham. https://doi.org/10.1007/978-3-030-37188-3_2

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