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

2026-09-21

AI Summary

This study addresses the lack of statistical significance validation for high-attention instances in attention-based multiple instance learning, where data-dependent selection invalidates conventional hypothesis testing. To overcome this, we propose an explicit statistical framework grounded in selective inference that reformulates the evaluation of high-attention instances as a hypothesis testing problem. By introducing an adaptive reference selection mechanism to correct selection bias and compute valid p-values, our approach circumvents the limitations of traditional over-conditioning methods and achieves rigorous Type I error control. Experiments on both synthetic datasets and whole-slide images (WSIs) demonstrate that the proposed framework attains substantially higher statistical power than standard approaches, thereby providing reliable statistical guarantees for model interpretability.

Abstract

Multiple instance learning (MIL) is widely used in computational pathology because it enables weakly supervised analysis of whole-slide images (WSIs) without requiring patch-level annotations. In attention-based MIL, instances with high attention scores are often interpreted as diagnostically important regions and used as visual explanations. However, attention scores alone cannot determine whether selected high-attention instances are significantly different from normal instances, limiting the reliability of attention-based explanations. In this paper, we formulate the evaluation of high-attention instances as a statistical hypothesis testing problem. Specifically, we assess whether a selected high-attention instance significantly deviates from a representative normal reference instance selected based on feature similarity. A major challenge is that both the target instance and the reference instance are selected through data-dependent procedures, rendering standard hypothesis testing invalid. To address this issue, we introduce a selective inference (SI) framework that explicitly accounts for the selection events induced by attention-based instance selection and adaptive reference selection, thereby enabling the computation of valid selective $p$-values conditional on these events. 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.

Problem

• Multiple Instance Learning
• Selective Inference
• Computational Pathology
• Statistical Hypothesis Testing
• Attention-based Explanation

Innovation

• Multiple Instance Learning
• Selective Inference
• Computational Pathology
• Statistical Hypothesis Testing
• Attention Mechanism