基于对比学习的编码器的脑胶质瘤亚区域分类
Patch-Level Glioblastoma Subregion Classification with a Contrastive Learning-Based Encoder
AI总结：
本文提出基于对比学习的编码器方法，在BraTS-Path 2025挑战中实现了脑胶质瘤亚区域分类的高精度表现。
摘要：
脑胶质瘤作为一种侵袭性脑肿瘤，其显著的分子和病理异质性使得诊断和患者分层变得复杂。尽管传统的组织病理学评估仍然是标准方法，但深度学习为整个滑动切片图像的客观和自动化分析提供了有前途的途径。在BraTS-Path 2025挑战中，我们开发了一种方法，该方法在官方训练数据集上对预训练的视觉Transformer（ViT）编码器和专用分类头进行微调。我们的模型在在线验证集上的表现，通过Synapse平台评估，得到了 Matthews 相关系数（MCC）为0.7064和F1分数为0.7676。在最终测试集上，模型实现了MCC为0.6509和F1分数为0.5330，这使我们的团队在BraTS-病理学2025挑战中获得第二名。我们的结果为基于ViT的组织病理学分析建立了坚实的基准，未来的工作将专注于缩小在未见过的验证数据上观察到的性能差距。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
