Patch-Level Glioblastoma Subregion Classification with a Contrastive Learning-Based Encoder

TL;DR

This paper presents a ViT-based deep learning approach for classifying glioblastoma subregions in histopathological images, achieving competitive results and securing second place in a challenging pathology competition.

Contribution

The study introduces a contrastive learning-based encoder fine-tuned for glioblastoma subregion classification, establishing a strong baseline for ViT in histopathology analysis.

Findings

Achieved MCC of 0.7064 and F1 of 0.7676 on validation set.

Secured second place in the BraTS-Pathology 2025 Challenge.

Provided a baseline for future ViT-based histopathological methods.

Abstract

The significant molecular and pathological heterogeneity of glioblastoma, an aggressive brain tumor, complicates diagnosis and patient stratification. While traditional histopathological assessment remains the standard, deep learning offers a promising path toward objective and automated analysis of whole slide images. For the BraTS-Path 2025 Challenge, we developed a method that fine-tunes a pre-trained Vision Transformer (ViT) encoder with a dedicated classification head on the official training dataset. Our model's performance on the online validation set, evaluated via the Synapse platform, yielded a Matthews Correlation Coefficient (MCC) of 0.7064 and an F1-score of 0.7676. On the final test set, the model achieved an MCC of 0.6509 and an F1-score of 0.5330, which secured our team second place in the BraTS-Pathology 2025 Challenge. Our results establish a solid baseline for ViT-based histopathological analysis, and future efforts will focus on bridging the performance gap observed on the unseen validation data.