S. Bakas

The Federated Tumor Segmentation and Classification (FeTS++) Challenge

International challenges have become the standard for developing and benchmarking biomedical image analysis methods using centralized multi-institutional data. Federated learning (FL) describes a paradigm-shift on multi-institutional collaborations, where models are trained across institutions while their data are alwa...

TRACE: Tissue and Report Atlas of Computational Embeddings

Computational pathology (CompPath) workflows increasingly rely on computational embeddings from digitized hema- toxylin and eosin (H&E) whole-slide images (WSIs). Generating such embeddings reproducibly and at scale requires large image transfers, magnification-aware tiling, model-specific preprocessing, versioned soft...

AI-Based Prognostic Risk Stratification of Adult-Type Diffuse Glioma Patients from H&E-Stained Slides

Background/Objectives: Adult-type diffuse gliomas (ADGs) are aggressive brain tumors with highly variable clinical outcomes. Prognostic assessment directly from routine hematoxylin and eosin (H&E)-stained whole-slide images (WSIs) could support personalized decision-making. Methods: To our knowledge, this is the first...

Multimodal Explainable Arti fi cial Intelligence for Prognostic Strati fi cation of Patients With Glioblastoma

Biomedical Imaging TRACE: Tissue and Report Atlas of Computational Embeddings

MRI Radiomics for Survival Prediction in Brain Metastases: A Machine Learning Analysis.

T1 postcontrast MRI radiomics showed limited standalone discrimination for overall survival in patients with brain metastases, which support cautious use of radiomics as an exploratory imaging biomarker and emphasize the need for integrated prognostic models that include clinical, treatment, molecular, and systemic dis...

Benchmarking Vision-Language Models for Automated Pathology Diagnosis and Report Generation

A clinically curated Pan-Asia WSI--report dataset is introduced and the REG 2025 benchmark is established as a benchmark for evaluating WSI-based structured report generation and vision-language understanding in computational pathology, providing insights for the design of clinically grounded multimodal pathology model...

Explainable Artificial Intelligence (XAI) in Computational Pathology: Definitions, Taxonomy, and Recommendations

This review aims to formalize XAI methods in CompPath through the introduction of a pathology-centric vocabulary comprising seven core terms and establishment of a task-driven framework that maps five clinical questions to recommended methods, method evaluation, and deployment context.