452 citations · 1k across the 25 of their papers we have counts for
5 papers · 2 filters
SURE-VQA: Systematic Understanding of Robustness Evaluation in Medical VQA Tasks
Kim-Celine Kahl, Selen Erkan, Jeremias Traub +4
Vision-Language Models (VLMs) have great potential in medical tasks, like Visual Question Answering (VQA), where they could act as interactive assistants for both patients and clin…
RadioActive: 3D Radiological Interactive Segmentation Benchmark
Constantin Ulrich, Tassilo Wald, Emily Tempus +3
Effortless and precise segmentation with minimal clinician effort could greatly streamline clinical workflows. Recent interactive segmentation models, inspired by METAs Segment Any…
Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?
Pedro R. A. S. Bassi, Wenxuan Li, Yucheng Tang +50
How can we test AI performance? This question seems trivial, but it isn't. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified…
nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation
Fabian Isensee, Tassilo Wald, Constantin Ulrich +4
The release of nnU-Net marked a paradigm shift in 3D medical image segmentation, demonstrating that a properly configured U-Net architecture could still achieve state-of-the-art re…
ValUES: A Framework for Systematic Validation of Uncertainty Estimation in Semantic Segmentation
Kim-Celine Kahl, Carsten T. Lüth, Maximilian Zenk +2
Uncertainty estimation is an essential and heavily-studied component for the reliable application of semantic segmentation methods. While various studies exist claiming methodologi…