Go to DBLP homepagePCR-MIL: Phenotype Clustering Reinforced Multiple Instance Learning for Whole Slide Image Classification
Abstract: Multiple instance learning (MIL) has proven effective in classifying whole slide images (WSIs), owing to its weakly supervised learning framework. However, existing MIL methods still face challenges, particularly over-fitting due to small sample sizes or limited WSIs (bags). Pseudo-bags enhance MIL’s classification performance by increasing the number of training bags. However, these methods struggle with noisy labels, as positive patches often occupy small portions of tissue, and pseudo-bags are typically generated by random splitting. Additionally, they face difficulties with non-discriminative instance embeddings due to the lack of domain-specific feature extractors. To address these limitations, we propose Phenotype Clustering Reinforced Multiple Instance Learning (PCR-MIL), a novel MIL framework that integrates clustering-based pseudo-bags to improve MIL’s noise robustness and the discriminative power of instance embeddings. PCR-MIL introduces two key innovations: (i) Phenotype Clustering-based Feature Selection (PCFS) selects relevant instance embeddings for prediction. It clusters instances into phenotype-specific groups, assigns positive instances to each pseudo-bag, and then uses Grad-CAM to select the most relevant positive embeddings. This approach mitigates noisy label challenges and enhances MIL’s robustness to noise; (ii) Reinforced Feature Extractor (RFE) uses reinforcement learning to train an extractor based on selected clean pseudo-bags instead of noisy samples. This approach improves the discriminative power of extracted instance embeddings and enhances the feature representation capabilities of MIL. Experimental results on the publicly available BRACS and CRC-DX datasets demonstrate that PCR-MIL outperforms state-of-the-art methods. The code is available at: https://github.com/JingjiaoLou/PCR-MIL.
External IDs:dblp:conf/miccai/LouPYJ25
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