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From the 1 of 46 linked papers with an AI index.

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

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

collaborators

44 papers

cs.CV2026

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…

eess.IV2026

Projected Energy Matching for Generative 3D Priors

Daniel Barco, Michal Balcerak, Suprosanna Shit +4

Energy Matching has emerged as a powerful generative framework that combines flow model efficiency with the explicit likelihood of Energy-Based Models (EBMs) via a single, time-ind…

cs.CV2026

VesselTok: Tokenizing Vessel-like 3D Biomedical Graph Representations for Reconstruction and Generation

Chinmay Prabhakar, Bastian Wittmann, Tamaz Amiranashvili +6

Spatial graphs provide a lightweight and elegant representation of curvilinear anatomical structures such as blood vessels, lung airways, and neuronal networks. Accurately modeling…

cs.LG2026

Graph Energy Matching: Transport-Aligned Energy-Based Modeling for Graph Generation

Michal Balcerak, Suprosanna Shit, Chinmay Prabhakar +4

Generative modeling of discrete data, such as graphs, underpins many scientific and industrial applications, including molecular discovery and materials design. In these domains, p…

cs.CV2026

Hide-and-Seek Attribution: Weakly Supervised Segmentation of Vertebral Metastases in CT

Matan Atad, Alexander W. Marka, Lisa Steinhelfer +10

Accurate segmentation of vertebral metastasis in CT is clinically important yet difficult to scale, as voxel-level annotations are scarce and both lytic and blastic lesions often r…

eess.IV2026

In search of truth: Evaluating concordance of AI-based anatomy segmentation models

Lena Giebeler, Deepa Krishnaswamy, David Clunie +9

Purpose AI-based methods for anatomy segmentation can help automate characterization of large imaging datasets. The growing number of similar in functionality models raises the cha…