Towards Visually Explaining Statistical Tests with Applications in Biomedical Imaging

TL;DR

This paper introduces an explainable deep statistical testing framework for biomedical imaging that provides sample-level and feature-level insights, enhancing interpretability of two-sample tests without relying on labels.

Contribution

It presents a novel method that combines deep two-sample testing with explainability, revealing influential samples and features in biomedical imaging data.

Findings

Identifies key image regions associated with disease variation.

Highlights influential samples driving statistical differences.

Provides spatial and instance-wise interpretability in biomedical tests.

Abstract

Deep neural two-sample tests have recently shown strong power for detecting distributional differences between groups, yet their black-box nature limits interpretability and practical adoption in biomedical analysis. Moreover, most existing post-hoc explainability methods rely on class labels, making them unsuitable for label-free statistical testing settings. We propose an explainable deep statistical testing framework that augments deep two-sample tests with sample-level and feature-level explanations, revealing which individual samples and which input features drive statistically significant group differences. Our method highlights which image regions and which individual samples contribute most to the detected group difference, providing spatial and instance-wise insight into the test's decision. Applied to biomedical imaging data, the proposed framework identifies influential samples and highlights anatomically meaningful regions associated with disease-related variation. This work bridges statistical inference and explainable AI, enabling interpretable, label-free population analysis in medical imaging.