用对比学习微调ViT，自动分类胶质母细胞瘤亚区
Patch-Level Glioblastoma Subregion Classification with a Contrastive Learning-Based Encoder
- 基于对比学习的ViT编码器，专用于病理切片亚区分类
- 在公开测试集上达到0.6509的MCC和0.5330的F1分数
- 为视觉Transformer在病理图像分析中的应用提供可靠基线
胶质母细胞瘤作为一种高度异质的脑部恶性肿瘤，其分子与病理特征复杂，给诊断和患者分层带来挑战。尽管传统组织病理学评估仍是标准方法，深度学习为全幻灯片图像的客观自动化分析提供了前景。针对BraTS-Path 2025挑战赛，我们开发了一种方法：在官方训练数据集上对预训练的Vision Transformer（ViT）编码器进行微调，并添加专用分类头。模型在Synapse平台评估的在线验证集上取得0.7064的马修斯相关系数（MCC）和0.7676的F1分数；在最终测试集上，获得0.6509的MCC和0.5330的F1分数，位列该挑战赛第二名。结果确立了基于ViT的病理图像分析的坚实基线，未来工作将聚焦于缩小在未见验证数据上的性能差距。
原文摘要 · Abstract (English)
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.
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