Deep Learning and the Future of Biomedical Image Analysis

  • Monika JyotiyanaEmail author
  • Nishtha Kesswani
Part of the Studies in Big Data book series (SBD, volume 68)


Deep Learning (DL) is popular among the researchers and academicians due to its reliability and accuracy, especially in the field of engineering and medical sciences. In the field of medical imaging for the diagnosis of disease, DL techniques are very helpful for early detection. Most important features of DL techniques are that they are uncomplicated with lower complexity, which ultimately saves the time and money and tackle many tough tasks simultaneously. Artificial Intelligence (AI) and Deep Learning (DL) technologies have rapidly improved in recent years. These techniques played an important role in every field of application, especially in the medical field such as in image processing, image fusion, image segmentation, image retrieval, image analysis, computer aided diagnosis (CAD), image registration and, image-guided therapy and many more. The aim of writing this chapter is to describe the DL methods and, the future of biomedical imaging using DL in detail and discuss the issues and challenges.


Machine Learning Deep Learning Convolutional Neural Networks Recurrent Neural Network Computer-Aided Diagnosis 


  1. 1.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)CrossRefGoogle Scholar
  2. 2.
    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)CrossRefGoogle Scholar
  3. 3.
    Stoyanov, D., Taylor, Z., Sarikaya, D., McLeod, J., Ballester, M.A.G., Codella, N.C., De Ribaupierre, S. (eds.): OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis: First International Workshop, OR 2.0 2018, 5th International Workshop, CARE 2018, 7th International Workshop, CLIP 2018, Third International Workshop, ISIC 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16 and 20, 2018, Proceedings, vol. 11041. Springer (2018)Google Scholar
  4. 4.
    Havaei, M., Guizard, N., Larochelle, H., Jodoin, P.M.: Deep learning trends for focal brain pathology segmentation in MRI. In: Machine Learning for Health Informatics, pp. 125–148. Springer, Cham (2016)Google Scholar
  5. 5.
    Nie, D., Zhang, H., Adeli, E., Liu, L., Shen, D.: 3D deep learning for multi-modal imaging-guided survival time prediction of brain tumor patients. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 212–220, Oct 2016. Springer, ChamCrossRefGoogle Scholar
  6. 6.
    Xu, T., Zhang, H., Huang, X., Zhang, S., Metaxas, D.N.: Multimodal deep learning for cervical dysplasia diagnosis. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 115–123, Oct 2016. Springer, ChamCrossRefGoogle Scholar
  7. 7.
    Cao, Y., Liu, C., Liu, B., Brunette, M.J., Zhang, N., Sun, T., Curioso, W.H.: Improving tuberculosis diagnostics using deep learning and mobile health technologies among resource-poor and marginalized communities. In: 2016 IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE), pp. 274–281, June 2016. IEEEGoogle Scholar
  8. 8.
    Anthimopoulos, M., Christodoulidis, S., Ebner, L., Christe, A., Mougiakakou, S.: Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Trans. Med. Imaging 35(5), 1207–1216 (2016)CrossRefGoogle Scholar
  9. 9.
    van Grinsven, M.J., van Ginneken, B., Hoyng, C.B., Theelen, T., Sánchez, C.I.: Fast convolutional neural network training using selective data sampling: application to hemorrhage detection in color fundus images. IEEE Trans. Med. Imaging 35(5), 1273–1284 (2016)CrossRefGoogle Scholar
  10. 10.
    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)CrossRefGoogle Scholar
  11. 11.
    Kleesiek, J., Urban, G., Hubert, A., Schwarz, D., Maier-Hein, K., Bendszus, M., Biller, A.: Deep MRI brain extraction: a 3D convolutional neural network for skull stripping. NeuroImage 129, 460–469 (2016)CrossRefGoogle Scholar
  12. 12.
    Avendi, M.R., 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)CrossRefGoogle Scholar
  13. 13.
    Singh, S., Maxwell, J., Baker, J.A., Nicholas, J.L., Lo, J.Y.: Computer-aided classification of breast masses: performance and interobserver variability of expert radiologists versus residents. Radiology 258(1), 73–80 (2011)CrossRefGoogle Scholar
  14. 14.
    Sahiner, B., Chan, H.P., Roubidoux, M.A., Hadjiiski, L.M., Helvie, M.A., Paramagul, C., Blane, C.: Malignant and benign breast masses on 3D US volumetric images: effect of computer-aided diagnosis on radiologist accuracy. Radiology 242(3), 716–724 (2007)CrossRefGoogle Scholar
  15. 15.
    Joo, S., Yang, Y.S., Moon, W.K., Kim, H.C.: Computer-aided diagnosis of solid breast nodules: use of an artificial neural network based on multiple sonographic features. IEEE Trans. Med. Imaging 23(10), 1292–1300 (2004)CrossRefGoogle Scholar
  16. 16.
    Chen, C.M., Chou, Y.H., Han, K.C., Hung, G.S., Tiu, C.M., Chiou, H.J., Chiou, S.Y.: Breast lesions on sonograms: computer-aided diagnosis with nearly setting-independent features and artificial neural networks. Radiology 226(2), 504–514 (2003)CrossRefGoogle Scholar
  17. 17.
    Sun, T., Zhang, R., Wang, J., Li, X., Guo, X.: Computer-aided diagnosis for early-stage lung cancer based on longitudinal and balanced data. PLoS ONE 8(5), e63559 (2013)CrossRefGoogle Scholar
  18. 18.
    Newell, D., Nie, K., Chen, J.H., Hsu, C.C., Hon, J.Y., Nalcioglu, O., Su, M.Y.: Selection of diagnostic features on breast MRI to differentiate between malignant and benign lesions using computer-aided diagnosis: differences in lesions presenting as mass and non-mass-like enhancement. Eur. Radiol. 20(4), 771–781 (2010)CrossRefGoogle Scholar
  19. 19.
    Tourassi, G.D., Frederick, E.D., Markey, M.K., Floyd, C.E.: Application of the mutual information criterion for feature selection in computer-aided diagnosis. Med. Phys. 28(12), 2394–2402 (2001)CrossRefGoogle Scholar
  20. 20.
    Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.Z.: Deep learning for health informatics. IEEE J. Biomed. Health Inform. 21(1), 4–21 (2017)CrossRefGoogle Scholar
  21. 21.
    Lu, J., Getz, G., Miska, E.A., Alvarez-Saavedra, E., Lamb, J., Peck, D., Downing, J.R.: MicroRNA expression profiles classify human cancers. Nature 435(7043), 834 (2005)CrossRefGoogle Scholar
  22. 22.
    Cruz-Roa, A.A., Ovalle, J.E.A., Madabhushi, A., Osorio, F.A.G.: A deep learning architecture for image representation, visual interpretability and automated basal-cell carcinoma cancer detection. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 403–410, Sept 2013. Springer, Berlin, HeidelbergCrossRefGoogle Scholar
  23. 23.
    Bowles, C., Qin, C., Guerrero, R., Gunn, R., Hammers, A., Dickie, D.A., Rueckert, D.: Brain lesion segmentation through image synthesis and outlier detection. NeuroImage Clin. 16, 643–658 (2017)CrossRefGoogle Scholar
  24. 24.
    Shen, D., Wu, G., Suk, H.I.: Deep learning in medical image analysis. Annu. Rev. Biomed. Eng. 19, 221–248 (2017)CrossRefGoogle Scholar
  25. 25.
    Chen, H., Dou, Q., Wang, X., Qin, J., Heng, P.A.: Mitosis detection in breast cancer histology images via deep cascaded networks. In: Thirtieth AAAI Conference on Artificial Intelligence, Feb 2016Google Scholar
  26. 26.
    Van Leemput, K., Maes, F., Vandermeulen, D., Colchester, A., Suetens, P.: Automated segmentation of multiple sclerosis lesions by model outlier detection. IEEE Trans. Med. Imaging 20(8), 677–688 (2001)CrossRefGoogle Scholar
  27. 27.
    Abraham, N., Khan, N.M.: A novel focal Tversky loss function with improved attention U-Net for lesion segmentation. arXiv preprint arXiv:1810.07842 (2018)
  28. 28.
    Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., Larochelle, H.: Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18–31 (2017)CrossRefGoogle Scholar
  29. 29.
    Sudre, C.H., Li, W., Vercauteren, T., Ourselin, S., Cardoso, M.J.: Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, pp. 240–248. Springer, Cham (2017)CrossRefGoogle Scholar
  30. 30.
    Yuan, Y.: Automatic skin lesion segmentation with fully convolutional-deconvolutional networks. arXiv preprint arXiv:1703.05165 (2017)
  31. 31.
    Litjens, G., Kooi, T., Bejnordi, B.E., Setio, A.A.A., Ciompi, F., Ghafoorian, M., Sánchez, C.I.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)CrossRefGoogle Scholar
  32. 32.
    Lundervold, A.S., Lundervold, A.: An overview of deep learning in medical imaging focusing on MRI. Z. Med. Phys. (2018)Google Scholar
  33. 33.
    Maclaren, J., Herbst, M., Speck, O., Zaitsev, M.: Prospective motion correction in brain imaging: a review. Magn. Reson. Med. 69(3), 621–636 (2013)CrossRefGoogle Scholar
  34. 34.
    Zaitsev, M., Akin, B., LeVan, P., Knowles, B.R.: Prospective motion correction in functional MRI. NeuroImage 154, 33–42 (2017)CrossRefGoogle Scholar
  35. 35.
    Juneja, K., Verma, A., Goel, S., Goel, S.: A survey on recent image indexing and retrieval techniques for low-level feature extraction in CBIR systems. In: 2015 IEEE International Conference on Computational Intelligence & Communication Technology, pp. 67–72, Feb 2015. IEEEGoogle Scholar
  36. 36.
    Pizarro, R., Assemlal, H.E., De Nigris, D., Elliott, C., Antel, S., Arnold, D., Shmuel, A.: Using deep learning algorithms to automatically identify the brain MRI contrast: implications for managing large databases. Neuroinformatics 17(1), 115–130 (2019)CrossRefGoogle Scholar
  37. 37.
    Sklan, J.E., Plassard, A.J., Fabbri, D., Landman, B.A.: Toward content-based image retrieval with deep convolutional neural networks. In: Medical Imaging 2015: Biomedical Applications in Molecular, Structural, and Functional Imaging, vol. 9417, p. 94172C, Mar 2015. International Society for Optics and PhotonicsGoogle Scholar
  38. 38.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
  39. 39.
    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
  40. 40.
    Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)Google Scholar
  41. 41.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)Google Scholar
  42. 42.
    Srivastava, R.K., Greff, K., Schmidhuber, J.: Training very deep networks. In: Advances in Neural Information Processing Systems, pp. 2377–2385 (2015)Google Scholar
  43. 43.
    Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)Google Scholar
  44. 44.
    Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1492–1500 (2017)Google Scholar
  45. 45.
    Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)Google Scholar
  46. 46.
    Zoph, B., Vasudevan, V., Shlens, J., Le, Q.V.: Learning transferable architectures for scalable image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8697–8710 (2018)Google Scholar
  47. 47.
    Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)Google Scholar
  48. 48.
    Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Bengio, Y.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)Google Scholar
  49. 49.
    Koch, G., Zemel, R., Salakhutdinov, R.: Siamese neural networks for one-shot image recognition. In: ICML Deep Learning Workshop, vol. 2, July 2015Google Scholar
  50. 50.
    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, Oct 2015. Springer, ChamGoogle Scholar
  51. 51.
    Milletari, F., Navab, N., Ahmadi, S.A.: V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565–571, Oct 2016. IEEEGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Central University of RajasthanBandar Sindri, AjmerIndia

Personalised recommendations