Mamba-Based Cross-Domain Adaptation for Robust Medical Image Classification
Keywords:
medical image classification; domain adaptation; state space models; Mamba; robust artificial intelligence; clinical deploymentAbstract
Deep learning has substantially improved medical image classification, yet performance frequently degrades when models trained on controlled datasets are applied across different scanners, institutions, patient populations, and acquisition protocols. Transformer-based architectures have increased representational capacity, but their quadratic attention cost and data requirements present significant obstacles for resource-constrained clinical environments. Recent state space models, particularly the Mamba family, offer a promising alternative by combining linear-time sequence modeling with selective state transitions that can capture long-range dependencies in high-dimensional inputs. This paper presents a system-level examination of Mamba-based cross-domain adaptation for robust medical image classification. It analyzes how selective state space mechanisms can be integrated with adversarial, discrepancy-based, and self-supervised domain adaptation principles to improve resilience across heterogeneous imaging environments. Rather than focusing solely on model-level accuracy, the paper addresses architectural trade-offs, data governance, infrastructure design, deployment sustainability, interpretability, fairness, and regulatory policy implications. We argue that robust clinical adoption requires a sociotechnical systems perspective in which adaptation is treated as a property of the full pipeline rather than a standalone algorithmic fix. The paper identifies important research directions including calibration under distribution shift, energy-efficient inference, federated and decentralized adaptation, and alignment with evolving regulatory frameworks. Overall, the work provides a forward-looking framework for integrating Mamba-based sequence modeling with cross-domain adaptation to support more resilient, equitable, and sustainable medical image classification.
References
1. Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sanchez, C. I. (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
2. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 770–778). https://doi.org/10.1109/CVPR.2016.90
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.
4. Gu, A., & Dao, T. (2023). Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.00752. https://doi.org/10.48550/arXiv.2312.00752
5. Shamshad, F., Khan, S., Zamir, S. W., Khan, M. H., Hayat, M., Khan, F. S., & Fu, H. (2023). Transformers in medical imaging: A survey. Medical Image Analysis, 88, 102802. https://doi.org/10.1016/j.media.2023.102802
6. 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.
7. Ganin, Y., & Lempitsky, V. (2015). Unsupervised domain adaptation by backpropagation. In Proceedings of the 32nd International Conference on Machine Learning (pp. 1180–1189). PMLR.
8. Tzeng, E., Hoffman, J., Saenko, K., & Darrell, T. (2017). Adversarial discriminative domain adaptation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 7167–7176). https://doi.org/10.1109/CVPR.2017.761
9. Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., & Vaughan, J. W. (2010). A theory of learning from different domains. Machine Learning, 79(1–2), 151–175. https://doi.org/10.1007/s10994-009-5152-4
10. Raghu, M., Zhang, C., Kleinberg, J., & Bengio, S. (2019). Transfusion: Understanding transfer learning for medical imaging. In Advances in Neural Information Processing Systems (Vol. 32).
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. Nature Biomedical Engineering, 5(12), 1290–1302. https://doi.org/10.1038/s41551-021-00776-1
12. 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.
13. 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
14. Dao, T., Fu, D. Y., Ermon, S., Rudra, A., & Re, C. (2022). FlashAttention: Fast and memory-efficient exact attention with IO-awareness. In Advances in Neural Information Processing Systems (Vol. 35, pp. 16344–16359).
15. Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., & Guo, B. (2021). Swin Transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 10012–10022). https://doi.org/10.1109/ICCV48922.2021.00986
16. 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
17. Irvin, J., Rajpurkar, P., Ko, M., Yu, Y., Ciurea-Ilcus, S., Chute, C., Marklund, H., Haghgoo, B., Ball, R. L., Shpanskaya, K., Seekins, J., Mong, D. A., Halabi, S. S., Sandberg, J. K., Jones, R., Larson, D. B., Langlotz, C. P., 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 (Vol. 33, No. 1, pp. 590–597). https://doi.org/10.1609/aaai.v33i01.3301590
18. Johnson, A. E. W., Pollard, T. J., Berkowitz, S. J., Greenbaum, N. R., Lungren, M. P., Deng, C., Mark, R. G., & Horng, S. (2019). MIMIC-CXR: A large publicly available database of labeled chest radiographs. Scientific Data, 6(1), 317. https://doi.org/10.1038/s41597-019-0322-0
19. Liu, Y., Tian, Y., Zhao, Y., Yu, H., Xie, L., Wang, Y., Ye, Q., & Liu, Y. (2024). VMamba: Visual state space model. arXiv preprint arXiv:2401.10166. https://doi.org/10.48550/arXiv.2401.10166
20. Tay, Y., Dehghani, M., Bahri, D., & Metzler, D. (2023). Efficient transformers: A survey. ACM Computing Surveys, 55(6), 1–28. https://doi.org/10.1145/3530811
21. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), 1–35. https://doi.org/10.1145/3457607
22. Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., & Elhadad, N. (2015). Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1721–1730). https://doi.org/10.1145/2783258.2788613
23. Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R, Parikh, D., & Batra, D. (2017). Grad-CAM: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE International Conference on Computer Vision (pp. 618–626). https://doi.org/10.1109/ICCV.2017.74
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 International Journal of Clinical and Translational Medicine
This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
