works on

From the 2 of 34 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 · 14 across the 5 of their papers we have counts for

collaborators

34 papers

cs.CV2026

MRIComp4Flow: Compression of 3D Brain MRI for Training Multi-Modal Generative Models

Lisa K. Fischer, Mykhailo Riabets, Daniel Rueckert +3

Large-scale multi-modal MRI datasets impose substantial storage and I/O costs, limiting the training of 3D generative models on commodity infrastructure. While lossy compression is…

cs.LG20262 cited

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming

Jiazhen Pan, Bailiang Jian, Paul Hager +19

The paper presents a dynamic red‑teaming framework (DAS) that continuously stress‑tests large language models on health tasks for robustness, privacy, bias, and hallucination, reve…

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

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…

eess.IV2026

Beyond the LUMIR challenge: The pathway to foundational registration models

Junyu Chen, Shuwen Wei, Joel Honkamaa +33

Medical image challenges have played a transformative role in advancing the field, catalyzing innovation and establishing new performance benchmarks. Image registration, a foundati…

cs.CV2026

Optimizing Rank for High-Fidelity Implicit Neural Representations

Julian McGinnis, Florian A. Hölzl, Suprosanna Shit +6

Implicit Neural Representations (INRs) based on vanilla Multi-Layer Perceptrons (MLPs) are widely believed to be incapable of representing high-frequency content. This has directed…