Self-Supervised Mamba Representation Learning for Generalizable Medical Image Analysis
Keywords:
self-supervised learning; state-space models; medical image analysis; representation learning; clinical deployment; algorithmic fairness; infrastructure governanceAbstract
The increasing availability of large-scale medical imaging archives has created a pressing need for representation learning methods that remain clinically meaningful when supervision is sparse, domain shifts emerge, and deployment settings diverge from training conditions. This paper examines self-supervised Mamba representation learning as a system-level framework for generalizable medical image analysis. Rather than focusing narrowly on architectural novelty, the discussion addresses how selective state-space models, self-supervised pretraining objectives, and medical imaging workflows interact across compute infrastructure, data governance, robustness assessment, and clinical integration. The paper analyzes the structural trade-offs between transformer-based attention mechanisms and linear-time state-space sequence modeling, emphasizing the implications for high-resolution two-dimensional images, volumetric scans, and multi-view diagnostic protocols. It further considers how pretraining datasets, augmentation policies, and institutional data-sharing agreements shape representation generality and fairness. The analysis extends to deployment concerns, including model updating, energy consumption, interpretability, regulatory alignment, and the socio-technical conditions under which representation learners become durable clinical infrastructure. By approaching self-supervised Mamba models as embedded components of larger learning systems, the paper provides a cross-disciplinary perspective on their potential to improve generalization, reduce annotation burdens, and support equitable medical image analysis while exposing governance and sustainability requirements that remain underexamined in the literature.
References
1. He, K., Fan, H., Wu, Y., Xie, S., & Girshick, R. (2020). Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 9729–9738). https://doi.org/10.1109/CVPR42600.2020.00975
2. Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A simple framework for contrastive learning of visual representations. In Proceedings of the 37th International Conference on Machine Learning (pp. 1597–1607). PMLR.
3. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N. (2021). An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations. https://arxiv.org/abs/2010.11929
4. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. In Advances in Neural Information Processing Systems 30 (pp. 5998–6008).
5. Gu, A., & Dao, T. (2023). Mamba: Linear-time sequence modeling with selective state spaces. arXiv. https://arxiv.org/abs/2312.00752
6. Liu, Y., Tian, Y., Zhao, Y., Yu, H., Xie, L., Wang, Y., Ye, Q., & Liu, Y. (2024). VMamba: Visual state space model. arXiv. https://arxiv.org/abs/2401.10166
7. Xing, Z., Ye, T., Yang, Y., Liu, G., & Zhu, L. (2024). SegMamba: Long-range sequential modeling Mamba for 3D medical image segmentation. arXiv. https://arxiv.org/abs/2403.08460
8. Ma, J., Li, F., & Wang, B. (2024). U-Mamba: Enhancing long-range dependency for biomedical image segmentation. arXiv. https://arxiv.org/abs/2401.04722
9. Isensee, F., Jaeger, P. F., Kohl, S. A. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: A self-configuring method for deep learning-based biomedical image segmentation. Nature Methods, 18(2), 203–211. https://doi.org/10.1038/s41592-020-01008-z
10. Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 234–241). Springer. https://doi.org/10.1007/978-3-319-24574-4_28
11. Azizi, S., Mustafa, B., Ryan, F., Beaver, Z., Freyberg, J., Deaton, J., Loh, A., Karthikesalingam, A., Kornblith, S., Chen, T., Natarajan, V., & Norouzi, M. (2021). Big self-supervised models advance medical image classification. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 3478–3488). https://doi.org/10.1109/ICCV48922.2021.00347
12. Zhou, Z., Sodha, V., Pang, J., Gotway, M. B., & Liang, J. (2021). Models Genesis. Medical Image Analysis, 67, 101840. https://doi.org/10.1016/j.media.2020.101840
13. Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Langlotz, C., Shpanskaya, K., Lungren, M. P., & Ng, A. Y. (2017). CheXNet: Radiologist-level pneumonia detection on chest X-rays with deep learning. arXiv. https://arxiv.org/abs/1711.05225
14. Irvin, J., Rajpurkar, P., Ko, M., Yu, Y., Ciurea-Ilcus, S., Chute, C., Marklund, H., Haghgoo, B., Ball, R., Shpanskaya, K., Seekins, J., Mong, D. A., Halabi, S. S., Sandberg, J. K., Jones, R., Larson, D., Langlotz, C., Patel, B. N., Lungren, M. P., & Ng, A. Y. (2019). CheXpert: A large chest radiograph dataset with uncertainty labels and expert comparison. In Proceedings of the AAAI Conference on Artificial Intelligence, 33(1), 590–597. https://doi.org/10.1609/aaai.v33i01.3301590
15. Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H. R., & Xu, D. (2022). UNETR: Transformers for 3D medical image segmentation. arXiv. https://arxiv.org/abs/2103.10504
16. Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., Kim, R., Raman, R., Nelson, P. C., Mega, J. L., & Webster, D. R. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA, 316(23), 2402–2410. https://doi.org/10.1001/jama.2016.17216
17. Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453. https://doi.org/10.1126/science.aax2342
18. Zheng, X., Chen, X., Gong, S., Griffin, X., & Slabaugh, G. (2025, September). Xfmamba: Cross-fusion mamba for multi-view medical image classification. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 672-682). Cham: Springer Nature Switzerland.
19. Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A. C., Lo, W.-Y., Dollár, P., & Girshick, R. (2023). Segment anything. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 4015–4026). https://doi.org/10.1109/ICCV51070.2023.00371
20. Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J. Q., Demszky, D., ... Liang, P. (2021). On the opportunities and risks of foundation models. arXiv. https://arxiv.org/abs/2108.07258
21. Raji, I. D., Gebru, T., Mitchell, M., Buolamwini, J., Lee, J., & Denton, E. (2020). Saving face: Investigating the ethical concerns of facial recognition auditing. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (pp. 145–151). https://doi.org/10.1145/3375627.3375820
22. Chen, X., Xie, S., & He, K. (2021). An empirical study of training self-supervised vision transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 9640–9649). https://doi.org/10.1109/ICCV48922.2021.00950
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Bioinformatics Insights and Analytics
This work is licensed under a Creative Commons Attribution 4.0 International License.