Learning Morphological Evidence Distributions for Whole-Slide Image Classification

Whole-slide image (WSI) classification based on multiple-instance learning is weakly supervised because each slide contains thousands of tile instances but typically has only a slide-level label. Standard attention pooling only selects informative instances, yet the pooled continuous feature does not by itself identify the recurring morphology that supports or opposes a class. To explicitly identify which recurring morphological patterns support or oppose each diagnosis, we introduce Morphology Evidence Learning via Distributions (MELD), a multiple-instance model in which an attention module determines which tiles to collect information from, a shared tokenizer maps tile features to distributions over morphology tokens, and class-conditional pointwise mutual information (PMI) assigns signed diagnostic evidence to these tokens for both prediction and interpretation. Across four WSI benchmarks and two frozen feature encoders, MELD achieves the best Macro-F1 on six of eight dataset–encoder settings. Pathologist review shows that tiles assigned to the learned morphology tokens consistently exhibit clinically recognizable morphological patterns. Moreover, projecting the PMI evidence back to individual tiles provides localization that outperforms localization based on the attention maps of representative MIL baselines. Finally, MELD can directly guide rate allocation for WSI compression using tumor-class PMI, assigning higher JPEG quality factors to tiles with positive tumor-class evidence and a low quality factor to all remaining tiles, achieving classification performance comparable to that of uniform JPEG compression with up to a \(2.33\times\) reduction in bits per pixel (BPP). Code is available at https://anonymous.4open.science/r/MELD-8F5D/README.mdanonymous.4open.science/MELD.