arXiv:2604. 23435v2 Announce Type: replace-cross Abstract: Grading knee osteoarthritis (KOA) on plain radiographs is poorly reproducible across readers.
By Azmul A. Irfan, Nur Ahmad Khatim, Alfan Alfian Irfan, Achmad Zaki, Erike A. Suwarsono, Mansur M. Arief
arXiv:2608.18012v2 Announce Type: replace
Abstract: One of the most common reasons for anterior cruciate ligament (ACL) reconstruction failure is femoral tunnel malpositioning (ACL footprint center a...
By Ruida Cheng, Gabriel Gibson, Ali Uneri, Frances T. Sheehan, Barry Boden
arXiv:2608. 09182v1 Announce Type: cross Abstract: Accurate landmark localization in medical images is a fundamental step for quantitative clinical measurement and downstream analysis.
By Jingxian Xu, Yuhao Huang, Rusi Chen, Yanfeng Zhou, Dong Ni
Driven by the availability of large-scale datasets, Human Pose Estimation (HPE) plays a critical role in numerous downstream tasks. However, mainstream benchmarks exhibit severe representation bias, predominantly featuring able-bodied individuals.
arXiv:2609.16033v1 Announce Type: cross
Abstract: Cervical vertebral maturation (CVM) assessment plays a pivotal role in orthodontic diagnosis and determining the optimal timing of treatment, especia...
By Peng Wang, Wanzhen Song, Anli Wang, Xueshuo Xie, Xiaohang Guan, Tao Li
UniReg is a conditional unified model for medical image registration that adapts deformation field estimation based on anatomical priors, registration type constraints, and instance-specific features. It combines the precision of task‑specific learning with the generalization of traditional optimization, enabling effective alignment across diverse CT and MR scenarios within a single framework. Experiments show UniReg outperforms state‑of‑the‑art learning‑based methods in accuracy while providing strong cross‑scenario generalization and reducing training cost and model redundancy.
By Zi Li, Jianpeng Zhang, Tai Ma, Tony C. W. Mok, Yan-Jie Zhou, Zeli Chen, Xianghua Ye, Le Lu, Cheng Chen, Dakai Jin