Brain tumors represent a severe and life-threatening condition, demanding
precise diagnosis and tailored treatment strategies. This study advances
patient care with findings from rigorous multiple-instance-learning
experimentations across various feature extractors and aggregators in brain
tumor histopathology. It establishes new performance benchmarks in glioma
subtype classification across multiple datasets, including a novel dataset
focused on the Indian demographic (IPD-Brain), providing a valuable resource
for existing research. Using a ResNet-50, pretrained on histopathology
datasets, for feature extraction, combined with DTFD feature aggregator, our
approach achieves state-of-the-art AUCs of 88.08 on IPD-Brain and 95.81 on
TCGA-Brain dataset respectively for three-way glioma subtype classification.
Moreover, it establishes new benchmarks in grading and detecting IHC molecular
biomarkers (IDH1 (mutant R132H), TP53, ATRX, Ki-67) through H&E stained whole
slide images for the IPD-Brain dataset. The work also highlights a significant
correlation between the model decision-making processes and the diagnostic
reasoning of pathologists, underscoring its capability to mimic professional
diagnostic procedures.

该研究使用多个特征提取器和聚合器，通过对脑瘤组织病理学进行多实例学习实验，为精确诊断和个体化治疗策略提供了更新的性能基准，包括在印度人群中的新数据集 (IPD-Brain) 。利用经过组织病理学数据预训练的 ResNet-50 进行特征提取，并结合 DTFD 特征聚合器，该方法在三种脑胶质瘤亚型分类上分别在 IPD-Brain 和 TCGA-Brain 数据集上取得了 88.08 和 95.81 的 AUC 值。此外，该研究通过 H＆E 染色全切片图像在 IPD-Brain 数据集上建立了基于分级和检测 IHC 分子生物标记物（IDH1（突变 R132H），TP53，ATRX，Ki-67）的新基准，并突出了模型决策过程与病理学家诊断推理之间的显著相关性，强调了其模拟专业诊断程序的能力。