A mixture of experts (MoE) model to improve AI-based computational pathology prediction performance under variable levels of image blur.

ABSTRACT:

Background

AI-based models for analysis of histopathology whole slide images (WSIs) are now common. However, image quality, particularly unsharp areas of WSIs, impacts model performance. In this study we investigate the impact of blur on deep learning models for WSI analysis. We propose a mixture of experts (MoE) strategy that mitigates the impact of unsharp areas in WSIs on classification performance by combining predictions from multiple expert models trained on data with varying levels of blur.

Methods

The study included hematoxylin and eosin (H&E) stained WSIs from 2093 breast cancer patients. Classification of histological grades 1 and 3 was used as a primary benchmarking case, and prediction of immunohistochemistry (IHC) markers (ER, PR, HER2) from H&E as a secondary c

SUBMITTER: Xiang Y 

PROVIDER: S-EPMC12516837 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature