390 citations · 652 across the 17 of their papers we have counts for
6 papers · 1 filter
Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting
Maximilian Rokuss, Yannick Kirchhoff, Saikat Roy +9
Accurate segmentation of Multiple Sclerosis (MS) lesions in longitudinal MRI scans is crucial for monitoring disease progression and treatment efficacy. Although changes across tim…
Comparative Benchmarking of Failure Detection Methods in Medical Image Segmentation: Unveiling the Role of Confidence Aggregation
Maximilian Zenk, David Zimmerer, Fabian Isensee +4
Semantic segmentation is an essential component of medical image analysis research, with recent deep learning algorithms offering out-of-the-box applicability across diverse datase…
Real-World Federated Learning in Radiology: Hurdles to overcome and Benefits to gain
Markus R. Bujotzek, Ünal Akünal, Stefan Denner +17
Objective: Federated Learning (FL) enables collaborative model training while keeping data locally. Currently, most FL studies in radiology are conducted in simulated environments…
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…
Skeleton Recall Loss for Connectivity Conserving and Resource Efficient Segmentation of Thin Tubular Structures
Yannick Kirchhoff, Maximilian R. Rokuss, Saikat Roy +8
Accurately segmenting thin tubular structures, such as vessels, nerves, roads or concrete cracks, is a crucial task in computer vision. Standard deep learning-based segmentation lo…
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…