Machine-learning for quantitative histopathology of piglet intestinal tissues: challenges with limited training data.

ABSTRACT:

Introduction

Use of Machine learning (ML) is rapidly expanding in histopathology, offering the potential to reduce interobserver variability and improve quantitative assessment. However, large datasets and computational resources commonly used in toxicology and human medicine are often unavailable to the veterinary pathologist. This study aimed to evaluate the feasibility and limitations of applying supervised ML on histopathological samples with limited training data, exemplified by training an ML model to segment the intestinal wall into its histological layers.

Materials and methods

The study included 145 piglets from five age groups (4, 14, 25, 49, and 67 days). Full-wall samples from duodenum, jejunum and ileum were collected post-mortem, stained with H&E and digitized.

SUBMITTER: Becker CB 

PROVIDER: S-EPMC12536243 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature