CVPR 2023 - Interventional Bag Multi-Instance Learning On Whole-Slide Pathological Images

In this episode we discuss Interventional Bag Multi-Instance Learning On Whole-Slide Pathological Images by Tiancheng Lin, Zhimiao Yu, Hongyu Hu, Yi Xu, Chang Wen Chen. The paper proposes a new scheme called Interventional Bag Multi-Instance Learning (IBMIL) to improve the classification of whole slide pathological images. Existing methods focus on improving feature extraction and aggregation but may capture spurious correlations between bags and labels. IBMIL uses backdoor adjustment for interventional training to suppress bias caused by contextual priors and achieves consistent performance boosts, making it a state-of-the-art method. Code for IBMIL is available on GitHub.