Patch-Level Glioblastoma Subregion Classification with a Contrastive Learning-Based Encoder
This work addresses automated diagnosis and patient stratification for glioblastoma, an aggressive brain tumor, but is incremental as it applies an existing method to a new dataset.
The paper tackled the problem of classifying glioblastoma subregions from whole slide images by fine-tuning a pre-trained Vision Transformer encoder, achieving an MCC of 0.7064 and F1-score of 0.7676 on validation data and securing second place in the BraTS-Pathology 2025 Challenge.
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.