From the 1 of 3 papers with an AI index.
12 citations
- Technical University of MunichDE3 papers
- Ludwig-Maximilians-Universität MünchenDE2 papers
- Munich Center for Quantum Science and TechnologyDE2 papers
- UiT The Arctic University of NorwayNO2 papers
- AGH University of KrakowPL1 paper
- Athinoula A. Martinos Center for Biomedical ImagingUS1 paper
- Beijing Academy of Artificial IntelligenceCN1 paper
- BMW (Germany)DE1 paper
- Canon (United States)US1 paper
- Center for Excellence in Brain Science and Intelligence TechnologyCN1 paper
- Centre for Tactile Internet with Human-in-the-LoopDE1 paper
- Centre Hospitalier de l’Université de MontréalCA1 paper
3 papers
cs.CV2026★ 12 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.LG2026★ 2 cited
Symbolic Recovery of Differential Equations: The Identifiability Problem
Philipp Scholl, Aras Bacho, Holger Boche +1
Symbolic recovery of differential equations is the ambitious attempt at automating the derivation of governing equations with the use of machine learning techniques. In contrast to…
math.NA2026
A Variational Framework for the Complexity of PDE Solutions
Juan Esteban Suarez Cardona, Holger Boche, Gitta Kutyniok
Partial Differential Equations (PDEs) are fundamental mathematical models for describing physical phenomena, yet most PDEs of practical interest require numerical approximations. T…