Few-shot Medical Classification: Methods, Challenges and Future Directions
Journal: Journal of Clinical Medicine Research DOI: 10.32629/jcmr.v6i4.4798
Abstract
In the context of medical imaging, due to the high cost and time-consuming nature of expert annotation, the scarcity of available annotated data often leads to overfitting during network construction. Few-shot learning offers a promising solution to this issue and has attracted significant research attention in recent years. This survey provides a comprehensive review of the most advanced techniques in few-shot learning-based medical image classification. Depending on the underlying deep learning mechanisms, existing methods are categorized into four groups: transfer learning-based, meta-learning-based, data augmentation-based, and multi-modal-based methods. This article also systematically summarizes publicly available few-shot medical image datasets and outlines prospective research directions.
Keywords
few-shot learning, few-shot medical image classification, transfer learning, meta-learning, data augmentation, multi-modal
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[4] Ma, X., Wu, J., Xue, S., Yang, J., Zhou, C., Sheng, Q.Z., Xiong, H., Akoglu, L. A comprehensive survey on graph anomaly detection with deep learning. IEEE transactions on knowledge and data engineering, 2021, 35, 12012–12038.
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[6] Snell, J., Swersky, K., Zemel, R. Prototypical networks for few-shot learning. Advances in neural information processing systems, 2017, 30.
[7] Menze, B.H., Jakab, A., Bauer, S., et al.. The Brain Tumor Segmentation (BraTS) Challenge 2018. In Proceedings of the Medical Image Computing and Computer Assisted Intervention (MICCAI), 2018.
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[9] Ravi, S., et al. Few-shot learning for histopathological image analysis. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2019.
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[13] Chen, Z., et al. MetaFormer: Transformer-based embedding models for few-shot medical image classification. Medical Image Analysis, 2022, 82, 102605.
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[17] Fuller, H., Garcia, F., Flores, V. Efficient Few-Shot Medical Image Analysis via Hierarchical Contrastive Vision-Language Learning arXiv preprint arXiv:2501.09294, 2025.
[18] Zhou, X., et al. Domain-aware transfer learning for few-shot medical image classification. Medical Image Analysis 2021, 73, 102180. Ge, Z., Demyanov, S., Chakravorty, R., Bowling, A., Garnavi, R. Selective joint fine-tuning: Improving transfer learning for 444 medical image classification. In Proceedings of the Proceedings of the IEEE Conference on Computer Vision and Pattern 445 Recognition (CVPR), 2017.
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[22] Antoniou, A., Edwards, H., Storkey, A. How to train your MAML. In Proceedings of the International Conference on Learning Representations (ICLR), 2019.
[23] Chen, W.Y., Liu, Y.C., Kira, Z., Wang, Y.C.F., Huang, J.B. A closer look at few-shot classification. International Conference on Learning Representations (ICLR), 2019.
[24] Raghu, A., Raghu, M., Bengio, S., Vinyals, O. Rapid learning or feature reuse? Towards understanding the effectiveness of MAML. In Proceedings of the International Conference on Learning Representations (ICLR), 2020.
[25] Jamal, M.A., Qi, G.J. Task-agnostic meta-learning for few-shot learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
[26] Grant, E., Finn, C., Levine, S., Darrell, T., Griffiths, T. Recasting gradient-based meta-learning as hierarchical Bayes. In Proceedings of the International Conference on Learning Representations (ICLR), 2018.
[27] Shorten, C., Khoshgoftaar, T.M. A survey on image data augmentation for deep learning. Journal of Big Data, 2019, 6, 60.
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[30] Yang, L., Liu, B., Chen, Y. Augmentation with simulated speckle noise improves ultrasound image classification. Ultrasound in Medicine & Biology, 2022, 48, 532–540.
[31] Frid-Adar, M., Klang, E., Amitai, M., Goldberger, J., Greenspan, H. GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. Neurocomputing, 2018, 321, 321–331.
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