Deep learning models for cervical cancer subtyping using whole slide images.

ABSTRACT:

Objective

This study aims to develop and evaluate an artificial intelligence-based model for cervical cancer subtyping using whole-slide images (WSI), incorporating both patch-level and WSI-level analyses to enhance diagnostic accuracy.

Methods

A total of 438 whole slide images were retrieved from three databases, one public dataset for model training and two independent private datasets for evaluation of generalization. It is comprised of two consecutive stages: a patch-level prediction and a WSI-level prediction. Patch-level predictions were performed using the four convolutional neural networks model, while WSI-level predictions were based on five machine learning algorithms with three different aggregation methods. We compared the models in terms of discrimination (accur

SUBMITTER: Yan HY 

PROVIDER: S-EPMC12711552 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature