ArXiv Preprint
Multiple Instance learning (MIL) models have been extensively used in
pathology to predict biomarkers and risk-stratify patients from gigapixel-sized
images. Machine learning problems in medical imaging often deal with rare
diseases, making it important for these models to work in a label-imbalanced
setting. Furthermore, these imbalances can occur in out-of-distribution (OOD)
datasets when the models are deployed in the real-world. We leverage the idea
that decoupling feature and classifier learning can lead to improved decision
boundaries for label imbalanced datasets. To this end, we investigate the
integration of supervised contrastive learning with multiple instance learning
(SC-MIL). Specifically, we propose a joint-training MIL framework in the
presence of label imbalance that progressively transitions from learning
bag-level representations to optimal classifier learning. We perform
experiments with different imbalance settings for two well-studied problems in
cancer pathology: subtyping of non-small cell lung cancer and subtyping of
renal cell carcinoma. SC-MIL provides large and consistent improvements over
other techniques on both in-distribution (ID) and OOD held-out sets across
multiple imbalanced settings.
Dinkar Juyal, Siddhant Shingi, Syed Ashar Javed, Harshith Padigela, Chintan Shah, Anand Sampat, Archit Khosla, John Abel, Amaro Taylor-Weiner
2023-03-23
