Key Patches Are All You Need: A Multiple Instance Learning Framework For Robust Medical Diagnosis

D.J. Araújo, M.R. Verdelho, A. Bissoto, J.C. Nascimento, C. Santiago, C. Barata; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 5231-5240

Deep learning models have revolutionized the field of medical image analysis due to their outstanding performances. However they are sensitive to spurious correlations often taking advantage of dataset bias to improve results for in-domain data but jeopardizing their generalization capabilities. In this paper we propose to limit the amount of information these models use to reach the final classification by using a multiple instance learning (MIL) framework. MIL forces the model to use only a (small) subset of patches in the image identifying discriminative regions. This mimics the clinical procedures where medical decisions are based on localized findings. We evaluate our framework on two medical applications: skin cancer diagnosis using dermoscopy and breast cancer diagnosis using mammography. Our results show that using only a subset of the patches does not compromise diagnostic performance for in-domain data compared to the baseline approaches. However our approach is more robust to shifts in patient demographics while also providing more detailed explanations about which regions contributed to the decision. Code is available at: https://github.com/diogojpa99/Medical-Multiple-Instance-Learning