CoRe-BT：一种多模态放射科-病理科-文本基准，用于鲁棒性脑肿瘤分类
CoRe-BT: A Multimodal Radiology-Pathology-Text Benchmark for Robust Brain Tumor Typing
AI总结：
CoRe-BT提出一个多模态基准，用于在缺失模态条件下提升脑肿瘤分类的鲁棒性，通过整合MRI、病理图像和报告，推动多模态学习与表示学习的发展。
摘要：
准确的脑肿瘤分类需要整合异质的临床证据，包括磁共振成像（MRI）、组织病理学和病理报告，这些证据在诊断时往往不完整。我们介绍了CoRe-BT，一种跨模态的放射科-病理科-文本基准，用于脑肿瘤分类，旨在研究在缺失模态条件下的鲁棒多模态学习。数据集包含310名患者，其多序列脑MRI（T1、T1c、T2、FLAIR）包括95例配有配对的H&E染色全片病理图像和病理报告的病例。所有病例均标注了肿瘤类型和分级，MRI体积包含专家标注的肿瘤掩码，使区域感知建模和辅助学习任务成为可能。肿瘤被分为六个临床相关的类别，以捕捉常见和罕见胶质瘤亚型的异质性。我们通过比较仅MRI模型与在存在病理信息时结合病理信息的多模态方法，评估在不同模态可用性下的肿瘤分类。基线实验展示了多模态融合的可行性，并突出了在临床相关分类任务中各模态的互补贡献。CoRe-BT为在不完整临床数据的现实场景中推进多模态胶质瘤分类和表示学习提供了坚实的基础。
英文摘要：
Accurate brain tumor typing requires integrating heterogeneous clinical evidence, including magnetic resonance imaging (MRI), histopathology, and pathology reports, which are often incomplete at the time of diagnosis. We introduce CoRe-BT, a cross-modal radiology-pathology-text benchmark for brain tumor typing, designed to study robust multimodal learning under missing modality conditions. The dataset comprises 310 patients with multi-sequence brain MRI (T1, T1c, T2, FLAIR), including 95 cases with paired H&E-stained whole-slide pathology images and pathology reports. All cases are annotated with tumor type and grade, and MRI volumes include expert-annotated tumor masks, enabling both region-aware modeling and auxiliary learning tasks. Tumors are categorized into six clinically relevant classes capturing the heterogeneity of common and rare glioma subtypes. We evaluate tumor typing under variable modality availability by comparing MRI-only models with multimodal approaches that incorporate pathology information when present. Baseline experiments demonstrate the feasibility of multimodal fusion and highlight complementary modality contributions across clinically relevant typing tasks. CoRe-BT provides a grounded testbed for advancing multimodal glioma typing and representation learning in realistic scenarios with incomplete clinical 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。
