Explainable AI for computational pathology identifies model limitations and tissue biomarkers.

ABSTRACT:

Introduction

Deep learning models hold great promise for digital pathology, but their opaque decision-making processes undermine trust and hinder clinical adoption. Explainable AI methods are essential to enhance model transparency and reliability.

Methods

We developed HIPPO, an explainable AI framework that systematically modifies tissue regions in whole slide images to generate image counterfactuals, enabling quantitative hypothesis testing, bias detection, and model evaluation beyond traditional performance metrics. HIPPO was applied to a variety of clinically important tasks, including breast metastasis detection in axillary lymph nodes, prognostication in breast cancer and melanoma, and IDH mutation classification in gliomas. In computational experiments, HIPPO w

SUBMITTER: Kaczmarzyk JR 

PROVIDER: S-EPMC11398542 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature