Statistical Testing for Multiple Instance Learning via Selective Inference with Applications to Computational Pathology

Multiple instance learning (MIL) is widely used in computational pathology for weakly supervised analysis of whole-slide images (WSIs) without patch-level annotations. In attention-based MIL, high-attention instances are often interpreted as diagnostically important regions, but attention scores alone do not establish whether these instances significantly differ from normal tissue. We formulate this problem as a statistical hypothesis test that compares a selected high-attention instance with a representative normal reference selected based on feature similarity. Because both the target and reference instances are selected through data-dependent procedures, standard hypothesis testing is invalid. We therefore develop a selective inference (SI) framework that accounts for attention-based instance selection and adaptive reference selection, enabling valid selective $p$-values. Experiments demonstrate Type-I error control on synthetic and MNIST-based data and practical applicability to pathological WSIs, with higher statistical power than the conventional over-conditioning approach.