Fig. 1: Overview of the pathology image characterization tool with uncertainty-aware rapid evaluations (PICTURE).
From: Uncertainty-aware ensemble of foundation models differentiates glioblastoma from its mimics
A We collected 2141 pathology slides of formalin-fixed paraffin-embedded (FFPE) and frozen section CNS tissues from five medical centers, including Brigham and Women’s Hospital, Mayo Clinic, the Hospital of the University of Pennsylvania, Taipei Veterans General Hospital, and the Medical University of Vienna. B We employed pathology foundation models (CTransPath, UNI, Lunit, Phikon, Virchow2, CONCH, GPFM, mSTAR, and CHIEF) to extract concise representations of high-resolution pathology image features. These feature extractors were trained on diverse datasets without labels of pathology diagnosis. C To enhance the model’s generalizability across patient populations, we curated additional pathology images from neuropathology publications and incorporated them into model training and uncertainty quantification processes18,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90. D We focused on the classification of glioblastoma and primary CNS lymphoma (PCNSL) due to their clinical significance. We partitioned the development dataset (i.e., Mayo Clinic) into fivefolds. PICTURE integrates three distinct uncertainty quantification methods: (U.1) Bayesian inference, which leverages prototypical images to detect atypical pathology profiles; (U.2) deep ensembling, which aggregates predictions from multiple foundation models and refines whole-slide predictions by excluding uncertain tiles; and (U.3) normalizing flow, which identifies central nervous system (CNS) cancer types not present in the training dataset. The graphics in (A–C) and the layout of (D) were created in BioRender. Zhao, J. (2025) https://BioRender.com/v04o604.
