Abstract
While deep learning systems have provided breakthroughs in several tasks in the medical domain, they are still limited by the problem of dependency on the availability of training data. To counter this limitation, there is active research ongoing in few shot learning. Few shot learning algorithms aim to overcome the data dependency by exploiting the information available from a very small amount of data. In medical imaging, due to the rare occurrence of some diseases, there is often a limitation on the available data, as a result, to which the success of few shot learning algorithms can prove to be a significant advancement. In this chapter, the background and working of few shot learning algorithms are explained. The problem statement for few shot classification and segmentation is described. There is then a detailed study of the problems faced in medical imaging related to the availability of limited data. After establishing context, the recent advances in the application of few shot learning to medical imaging tasks such as classification and segmentation are explored. The results of these applications are examined with a discussion on its future scope.
Keywords
J. Kotia and A. Kotwal—Both authors have contributed equally to this chapter.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Razzak, M. I., Naz, S., & Zaib, A. (2018). Deep learning for medical image processing: overview, challenges and the future. In N. Dey, A. Ashour, & S. Borra (Eds.), Classification in BioApps (Vol. 26)., Lecture Notes in Computational Vision and Biomechanics Cham: Springer.
He, K., Zhang, X., Ren, S., Sun, J. (2015). Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In: Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV 2015) (pp. 1026–1034). IEEE Computer Society. https://doi.org/10.1109/ICCV.2015.123
Lee, J. G., et al. (2017). Deep learning in medical imaging: general overview. Korean Journal of Radiology, 18(4), 570–584. https://doi.org/10.3348/kjr.2017.18.4.570.
Litjens, G., et al. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60–88. https://doi.org/10.1016/j.media.2017.07.005. (ISSN 1361-8415).
Shen, D., Wu, G., & Suk, H.-I. (2017). Deep learning in medical image analysis. Annual Review of Biomedical Engineering, 19(1), 221–248. https://doi.org/10.1146/annurev-bioeng-071516-044442.
Miller, E. G., Matsakis, N. E., Viola, P. A. (2000). Learning from one example through shared densities on transforms. In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2000) (Cat. No.PR00662), Hilton Head Island, SC (Vol. 1, pp. 464–471). https://doi.org/10.1109/CVPR.2000.855856
Li, F.-F., Fergus, & Perona (2003). A Bayesian approach to unsupervised one-shot learning of object categories. In: Proceedings Ninth IEEE International Conference on Computer Vision, Nice, France (Vol. 2, pp. 1134–1141). https://doi.org/10.1109/ICCV.2003.1238476
Li, F.-F., Fergus, R., & Perona, P. (2006). One-shot learning of object categories. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(4), 594–611. https://doi.org/10.1109/TPAMI.2006.79.
Brenden, L., Ruslan, S., Jason, G., Joshua, T. (2011). One shot learning of simple visual concepts.
Koch, G. R. (2015). Siamese Neural Networks for One-Shot Image Recognition.
Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., & Lillicrap, T. (2016). One-shot learning with memory-augmented neural networks. In Proceedings of the 33nd International Conference on Machine Learning.
Ravi, S., & Larochelle, H. (2017). Optimization as a model for few-shot learning. In ICLR (2017).
Finn, C., Abbeel, P., & Levine, S. (2017). Model-agnostic meta-learning for fast adaptation of deep networks. In Proceedings of the 34th International Conference on Machine Learning - Volume 70 (ICML 2017) (pp. 1126–1135). JMLR.org.
Snell, J., Swersky, K., Zemel, R. (2017). Prototypical Networks for Few-shot Learning.
Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P., Hospedales, T. (2018). Learning to compare: relation network for few-shot learning (pp. 1199–1208). https://doi.org/10.1109/CVPR.2018.00131.
Kaiser, Ł., Nachum, O., Roy, A., & Bengio, S. (2017). Learning to Remember Rare Events.
Shaban, A., Bansal, S., Liu, Z., Essa, I., & Boots, B. (2017, September). One-shot learning for semantic segmentation. In T.K. Kim, S. Zafeiriou, G. Brostow & K. Mikolajczyk (Eds.), Proceedings of the British Machine Vision Conference (BMVC) (pp. 167.1–167.13). BMVA Press
Rakelly, K., Shelhamer, E., Darrell, T., Efros, A. A., & Levine, S. (2018). Conditional networks for few-shot semantic segmentation. In ICLR.
Rakelly, K., Shelhamer, E., Darrell, T., Efros, A., & Levine, S. (2018). Few-Shot Segmentation Propagation with Guided Networks.
Kim, J., Oh, T., Lee, S., Pan, F., & Kweon, I. (2019). Variational prototyping-encoder: one-shot learning with prototypical images. In CVPR.
Lee, K., Maji, S., Ravichandran, A., & Soatto, S. (2019). Meta-learning with differentiable convex optimization. In: CVPR.
Kim, J., Kim, T., Kim, S., & Yoo, C. D. (2019). Edge-labeling graph neural network for few-shot learning. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 11–20), Long Beach, CA, USA. https://doi.org/10.1109/CVPR.2019.00010
Jamal, M. A., Qi, G., & Shah, M. (2018). Task-agnostic meta-learning for few-shot learning. In: CVPR.
Sun, Q., Liu, Y., Chua, T., & Schiele, B. (2018). Meta-transfer learning for few-shot learning. In: CVPR.
Li, H., Eigen, D., Dodge, S.F., Zeiler, M.D., & Wang, X. (2019). Finding task-relevant features for few-shot learning by category traversal. In: CVPR.
Zhang, C., Lin, G., Liu, F., Yao, R., & Shen, C. (2019). CANet: class-agnostic segmentation networks with iterative refinement and attentive few-shot learning.
Zhang, H., Zhang, J., & Koniusz, P. (2019). Few-Shot Learning via Saliency-Guided Hallucination of Samples (pp. 2765–2774). https://doi.org/10.1109/CVPR.2019.00288.
Chen, Z., Fu, Y., Wang, Y., Ma, L., Liu, W., & Hebert, M. (2019). Image deformation meta-networks for one-shot learning. In: CVPR.
Schwartz, E., Karlinsky, L., Feris, R., Giryes, R., & Bronstein, A. (2019). Baby steps towards few-shot learning with multiple semantics.
Wang, X., Yu, F., Wang, R., Darrell, T., & Gonzalez, J. (2019). TAFE-Net: task-aware feature embeddings for low shot learning. In CVPR.
Puch, S., Sánchez, I., & Rowe, M. (2019). Few-shot learning with deep triplet networks for brain imaging modality recognition. In DART/MIL3ID@MICCAI.
Kim, M., Zuallaert, J., De Neve, W. (2017). Few-shot learning using a small-sized dataset of high-resolution FUNDUS images for glaucoma diagnosis. In Proceedings of the 2nd International Workshop on Multimedia for Personal Health and Health Care (MMHealth 2017) (pp. 89–92). New York: Association for Computing Machinery. https://doi.org/10.1145/3132635.3132650
Hu, S., Tomczak, J. (2018) Max Welling: Meta-Learning for Medical Image Classification.
Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: convolutional networks for biomedical image segmentation. In: N. Navab, J. Hornegger, W. Wells, A. Frangi (eds.), Medical Image Computing and Computer-Assisted Intervention (MICCAI 2015). Lecture Notes in Computer Science (vol. 9351). Cham: Springer.
Lahiani, A., Gildenblat, J., Klaman, I., Navab, N., & Klaiman, E. (2018). Generalizing multistain immunohistochemistry tissue segmentation using one-shot color deconvolution deep neural networks.
Guha Roy, A., Siddiqui, S., Pölsterl, S., Navab, N. & Wachinger, C. (2019). ‘Squeeze & Excite’ Guided Few-Shot Segmentation of Volumetric Images.
Goodfellow, I. J., et al. (2014). Generative adversarial nets. In Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2 (NIPS 2014) (pp. 2672–2680). Cambridge: MIT Press.
Mondal, A., Dolz, J., & Desrosiers, C. (2018). Few-shot 3D Multi-modal Medical Image Segmentation Using Generative Adversarial Learning.
Zhao, A., Balakrishnan, G., Durand, F., Guttag, J., & Dalca, A. (2019). Data augmentation using learned transforms for one-shot medical image segmentation.
Wang, Y., Yao, Q., Kwok, J. T., & Ni, L. M. (2019). Generalizing from a Few Examples: A Survey on Few-Shot Learning.
Thrun, S. (1998). Lifelong learning algorithms. In S. Thrun & L. Pratt (Eds.), Learning to Learn. Boston: Springer.
Ren, M., et al. (2018). Meta-Learning for Semi-Supervised Few-Shot Classification.
Vilalta, R., & Drissi, Y. (2002). A perspective view and survey of meta-learning. Artificial Intelligence Review, 18, 77–95. https://doi.org/10.1023/A:1019956318069.
Vilalta, R., Giraud-Carrier, C., Brazdil, P. (2010). Meta-Learning - Concepts and Techniques.
Kruspe, A. (2019). One-Way Prototypical Networks. https://doi.org/10.13140/RG.2.2.31516.95367.
Chung, Y.-A., & Weng, W.-H. (2017). Learning Deep Representations of Medical Images using Siamese CNNs with Application to Content-Based Image Retrieval.
Rios, A., & Kavuluru, R. (2018). Few-shot and zero-shot multi-label learning for structured label spaces. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing (pp. 3132–3142).
Chen, W., Liu, Y., Kira, Z., Wang, Y. F., & Huang, J. (2019). A closer look at few-shot classification. In International Conference on Learning Representations 2019. ArXiv, abs/1904.04232.
Dhillon, G. S., Chaudhari, P., Ravichandran, A., & Soatto, S. (2019). A baseline for few-shot image classification. In International Conference on Learning Representations 2020. ArXiv, abs/1909.02729.
London, A. (2019). Artificial intelligence and black-box medical decisions: accuracy versus explainability. The Hastings Center Report, 49, 15–21. https://doi.org/10.1002/hast.973.
Yoichi, H. (2019). The right direction needed to develop white-box deep learning in radiology, pathology, and ophthalmology: a short review. Frontiers in Robotics and AI, 6, 24. https://doi.org/10.3389/frobt.2019.00024.
Dey, N., et al. (2015). Parameter optimization for local polynomial approximation based intersection confidence interval filter using genetic algorithm: an application for brain MRI image de-noising. Journal of Imaging, 1, 60–84. https://doi.org/10.3390/jimaging1010060.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Kotia, J., Kotwal, A., Bharti, R., Mangrulkar, R. (2021). Few Shot Learning for Medical Imaging. In: Das, S., Das, S., Dey, N., Hassanien, AE. (eds) Machine Learning Algorithms for Industrial Applications. Studies in Computational Intelligence, vol 907. Springer, Cham. https://doi.org/10.1007/978-3-030-50641-4_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-50641-4_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-50640-7
Online ISBN: 978-3-030-50641-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)