Multicenter Histology Image Integration and Multiscale Deep Learning for Machine Learning-Enabled Pediatric Sarcoma Classification.

ABSTRACT: Pediatric sarcomas present diagnostic challenges due to their rarity and diverse subtypes, often requiring specialized pathology expertise and costly genetic tests. To overcome these barriers, we developed a computational pipeline leveraging deep learning methods to accurately classify pediatric sarcoma subtypes from digitized histology slides. To ensure classifier generalizability and minimize center-specific artifacts, we collected and harmonized a dataset comprising 867 whole slide images (WSIs) from three medical centers and the Children's Oncology Group (COG). Multiple convolutional neural network (CNN) and vision transformer (ViT) architectures were systematically evaluated as feature extractors for SAMPLER-based WSI representations, and input parameters such as tile size combination

SUBMITTER: Thiesen A 

PROVIDER: S-EPMC12204438 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature