4.2k citations · 11.8k across the 61 of their papers we have counts for
63 papers
The Federated Tumor Segmentation and Classification (FeTS++) Challenge
Wenyi Tang, Carla Pitarch-Abaigar, Siddhesh Thakur +5
International challenges have become the standard for developing and benchmarking biomedical image analysis methods using centralized multi-institutional data. Federated learning (…
Benchmarking Vision-Language Models for Automated Pathology Diagnosis and Report Generation
Yumi Lee, Harim Oh, Hyoryung Kim +52
The rapid advancement of vision-language models (VLMs) has accelerated progress in computational pathology; however, whole-slide image (WSI)-based pathology report generation remai…
Explainable Artificial Intelligence (XAI) in Computational Pathology: Definitions, Taxonomy, and Recommendations
Shubham Innani, Suhang You, Adam Shephard +13
Computational pathology (CompPath) is transforming medicine by leveraging artificial intelligence (AI) algorithms to support diagnosis, prognosis, and treatment prediction from gig…
The MICCAI Federated Tumor Segmentation (FeTS) Challenge 2024: Efficient and Robust Aggregation Methods for Federated Learning
Akis Linardos, Sarthak Pati, Ujjwal Baid +25
We present the design and results of the MICCAI Federated Tumor Segmentation (FeTS) Challenge 2024, which focuses on federated learning (FL) for glioma sub-region segmentation in m…
Medical Imaging AI Competitions Lack Fairness
Annika Reinke, Evangelia Christodoulou, Sthuthi Sadananda +34
Benchmarking competitions are central to the development of artificial intelligence (AI) in medical imaging, defining performance standards and shaping methodological progress. How…
Classifying Histopathologic Glioblastoma Sub-regions with EfficientNet
Sanyukta Adap, Ujjwal Baid, Spyridon Bakas
Glioblastoma (GBM) is the most common aggressive, fast-growing brain tumor, with a grim prognosis. Despite clinical diagnostic advancements, there have not been any substantial imp…