65 citations · 68 across the 4 of their papers we have counts for
4 papers
Scaling nnU-Net for CBCT Segmentation
Fabian Isensee, Yannick Kirchhoff, Lars Kraemer +3
This paper presents our approach to scaling the nnU-Net framework for multi-structure segmentation on Cone Beam Computed Tomography (CBCT) images, specifically in the scope of the…
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…
Mitigating False Predictions In Unreasonable Body Regions
Constantin Ulrich, Catherine Knobloch, Julius C. Holzschuh +7
Despite considerable strides in developing deep learning models for 3D medical image segmentation, the challenge of effectively generalizing across diverse image distributions pers…
SAM.MD: Zero-shot medical image segmentation capabilities of the Segment Anything Model
Saikat Roy, Tassilo Wald, Gregor Koehler +5
Foundation models have taken over natural language processing and image generation domains due to the flexibility of prompting. With the recent introduction of the Segment Anything…