Cell type prediction with neighborhood-enhanced cellular embedding using deep learning on hematoxylin and eosin-stained images.

ABSTRACT:

Purpose

This study aimed to predict the cell types that infiltrate the tumor microenvironment using hematoxylin and eosin-stained images from colon cancer and breast cancer samples.

Methods

Two datasets, one focused on colon cancer and the other on breast cancer, were used to develop deep learning models. Cell segmentation was performed using Stardist, followed by the K-Nearest Neighbor method to construct a neighborhood-enhanced cellular extraction matrix for model training. Transductive semi-supervised learning was applied to the breast cancer dataset, where the Base-4 model was trained on S1 and S2 samples and subsequently used to generate assigned labels for the S3, S4, and S5 sets, on which the Base-4+ model was trained.

Results

The Base-7 model trained on colon

SUBMITTER: Phan NN 

PROVIDER: S-EPMC12305602 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature