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From the 1 of 17 linked papers with an AI index.

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20242026
most citedThe TopCoW Challenge -- Topology-Aware Circle of Willis Segmentation for CT and MR Angiography

12 citations · 13 across the 3 of their papers we have counts for

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15 papers · 1 filter

cs.CV202612 cited

The TopCoW Challenge -- Topology-Aware Circle of Willis Segmentation for CT and MR Angiography

Kaiyuan Yang, Fabio Musio, Yihui Ma +112

The paper introduces the TopCoW Challenge, a benchmark for automatically segmenting the Circle of Willis in CT and MR angiography using deep learning, and provides a new annotated…

cs.CV2026

Lost in the Folds: When Cross-Validation Is Not a Deep Ensemble for Uncertainty Estimation

Tristan Kirscher, Markus Bujotzek, Yannick Kirchhoff +5

Ensemble disagreement is widely used as a proxy for epistemic uncertainty in medical image segmentation. In practice, many studies form ensembles via K-fold cross-validation (CV),…

cs.CV20261 cited

Primus: Enforcing Attention Usage for 3D Medical Image Segmentation

Tassilo Wald, Saikat Roy, Fabian Isensee +7

Transformers have achieved remarkable success across multiple fields, yet their impact on 3D medical image segmentation remains limited with convolutional networks still dominating…

cs.CV2026

nnLandmark: A Self-Configuring Method for 3D Medical Landmark Detection

Alexandra Ertl, Stefan Denner, Robin Peretzke +8

Landmark detection is central to many medical applications, such as identifying critical structures for treatment planning or defining control points for biometric measurements. Ho…

cs.CV2026

Finally Outshining the Random Baseline: A Simple and Effective Solution for Active Learning in 3D Biomedical Imaging

Carsten T. Lüth, Jeremias Traub, Kim-Celine Kahl +6

Active learning (AL) has the potential to drastically reduce annotation costs in 3D biomedical image segmentation, where expert labeling of volumetric data is both time-consuming a…

cs.CV2025

Expectation-Maximization as the Engine of Scalable Medical Intelligence

Wenxuan Li, Pedro R. A. S. Bassi, Tianyu Lin +19

Large, high-quality, annotated datasets are the foundation of medical AI research, but constructing even a small, moderate-quality, annotated dataset can take years of effort from…