Deep feature batch correction using ComBat for machine learning applications in computational pathology.

ABSTRACT:

Background

Developing artificial intelligence (AI) models for digital pathology requires large datasets from multiple sources. However, without careful implementation, AI models risk learning confounding site-specific features in datasets instead of clinically relevant information, leading to overestimated performance, poor generalizability to real-world data, and potential misdiagnosis.

Methods

Whole-slide images (WSIs) from The Cancer Genome Atlas (TCGA) colon (COAD), and stomach adenocarcinoma datasets were selected for inclusion in this study. Patch embeddings were obtained using three feature extraction models, followed by ComBat harmonization. Attention-based multiple instance learning models were trained to predict tissue-source site (TSS), as well as clinical and gen

SUBMITTER: Murchan P 

PROVIDER: S-EPMC11470259 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature