SC-MIL:用于病理学中不平衡分类的监督对比多实例学习 SC-MIL: Supervised Contrastive Multiple Instance Learning for Imbalanced Classification in Pathology

作者:Dinkar Juyal Siddhant Shingi Syed Ashar Javed Harshith Padigela Chintan Shah Anand Sampat Archit Khosla John Abel Amaro Taylor-Weiner


Multiple Instance learning (MIL) models have been extensively used inpathology to predict biomarkers and risk-stratify patients from gigapixel-sizedimages. Machine learning problems in medical imaging often deal with rarediseases, making it important for these models to work in a label-imbalancedsetting. Furthermore, these imbalances can occur in out-of-distribution (OOD)datasets when the models are deployed in the real-world. We leverage the ideathat decoupling feature and classifier learning can lead to improved decisionboundaries for label imbalanced datasets. To this end, we investigate theintegration of supervised contrastive learning with multiple instance learning(SC-MIL). Specifically, we propose a joint-training MIL framework in thepresence of label imbalance that progressively transitions from learningbag-level representations to optimal classifier learning. We performexperiments with different imbalance settings for two well-studied problems incancer pathology: subtyping of non-small cell lung cancer and subtyping ofrenal cell carcinoma. SC-MIL provides large and consistent improvements overother techniques on both in-distribution (ID) and OOD held-out sets acrossmultiple imbalanced settings.



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