EC-MIL: Disentangling Evidence and Context for Intrinsic Instance-Level Explanations

Modern multiple instance learning (MIL) models enhance bag-level prediction via instance interactions, but the same interactions entangle individual contributions and hinder instance-level attribution. This tension is particularly important in biomedicine, where instance-level supervision is scarce yet identifying supporting or opposing instances is essential. To address this, we introduce Evidence-Context Disentangled MIL (EC-MIL), a framework separating instance-local, class-specific evidence from bag-dependent context weighting. EC-MIL expresses each class logit as a weighted sum of instance evidence, preserving backbone context modeling while making individual contributions directly accessible. The product of each instance's evidence and context weight naturally defines Weighted Evidence (WE), an intrinsic, signed, class-specific attribution whose sum exactly recovers the class logit in a single forward pass. Under explicit assumptions, we justify this decomposition and show that excess population bag-level cross-entropy risk controls instance-contribution decomposition error. Across synthetic, whole-slide image, and mutation-bag tasks, EC-MIL maintains competitive predictive performance, while WE performs well in evidence recovery and instance-removal faithfulness at lower attribution cost than post-hoc alternatives. On mutation bags, WE also identifies gene-level signals consistent with established tissue–gene associations. Together, these results demonstrate that expressive context modeling and efficient intrinsic attribution can be achieved within a unified MIL framework. The code can be obtained from: https://anonymous.4open.science/r/EC-MIL-6449.