  The effective management of brain tumors relies on precise typing, subtyping,
and grading. 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 the Double-Tier
Feature Distillation (DTFD) feature aggregator, our approach achieves
state-of-the-art AUCs of 88.08 on IPD-Brain and 95.81 on the TCGA-Brain
dataset, respectively, for three-way glioma subtype classification. Moreover,
it establishes new benchmarks in grading and detecting IHC molecular biomarkers
(IDH1R132H, 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.
