Latent representation of H&E images retains biological information in a breast cancer cohort.

ABSTRACT: Imaging technologies and staining based pathology are important components of common practice cancer care. Specifically, H&E imaging is standard for almost all cancer patients. Traditionally, H&E images can serve, when used by experienced trained pathologists, to infer important biological properties of the samples. Recent work demonstrated that machine learning and machine vision analysis of H&E images can further expand the scope of the inference. However, H&E images are high-resolution, making them difficult to analyze and possibly noisy. In this work, we propose an autoencoder-based pipeline that greatly reduces the dimension of the data representation while maintaining valuable properties. In particular, we investigate how different latent space dimensions affect bulk label prediction

SUBMITTER: Benmussa C 

PROVIDER: S-EPMC12463230 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature