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From the 1 of 16 papers with an AI index.

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

12 citations

16 papers

cs.CV2026

OPERA: Offline Policy-guided Expert Routing and Adaptation for Universal Biomedical Image Analysis

Zihan Li, Feiyang Liu, Dandan Shan +2

Biomedical image analysis spans diverse modalities and tasks, yet real-world deployment is hindered by severe distribution shifts across scanners, protocols, and patient population…

cond-mat.mtrl-sci2026

Fe-doping-induced band structure modification and cryogenic phase stability in Cs2AgBiBr6 single crystals

Yanan Li, Xuejiao Wu, Jidong Deng +1

Despite its promise as a lead-free alternative, the practical application of Cs2AgBiBr6 in optoelectronics is limited by its wide band gap and detrimental intrinsic defects. To ove…

astro-ph.GA2026

Megaparsec-Scale Neutral Hydrogen Flows in the Neighborhood of Hickson Compact Group 100

Qingzheng Yu, Taotao Fang, Enrico M. Di Teodoro +2

The evolution of galaxies is strongly influenced by their ability to exchange gas with their surroundings, yet direct observational constraints on these processes remain scarce. Us…

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.RO2026

Learning Robot Visual Navigation in Crowds via Intention-Aware Scene Representations

Han Bao, Bingyi Xia, Hanjing Ye +5

Robot crowd navigation requires the ability to infer human intentions while accounting for the structural constraints of the environment. Currently, deep reinforcement learning (DR…

cs.LG2026

Federated Learning with Enhanced Privacy via Model Splitting and Random Client Participation

Yiwei Li, Shuai Wang, Zhuojun Tian +2

Federated Learning (FL) often adopts differential privacy (DP) to protect client data, but the added noise required for privacy guarantees can substantially degrade model accuracy.…