arXiv:2507. 21018v2 Announce Type: replace-cross Abstract: Automated assessment of human motion plays a vital role in rehabilitation, enabling objective evaluation of patient performance and progress.
By Ali Ismail-Fawaz, Maxime Devanne, Stefano Berretti, Jonathan Weber, Germain Forestier
arXiv:2606. 17437v1 Announce Type: cross Abstract: Automated classification of standard echocardiographic views is crucial for efficient clinical workflow but faces three main challenges.
By Bo Gou, Jicheng Zhang, Jianlong Xiong, Tao He, Bentian Liu, Hai Wu, Yijiao Wang, Yu Zhang, Yujia Yang, Yun Dai, Jian Liu, Jie Wang
Active surveillance (AS) is the preferred strategy for favorable-risk prostate cancer, yet current protocols rely on scheduled repeat biopsies, most of which reveal no progression and are unnecessary. Existing risk-stratification tools operate on single time-point imaging or depend on explicit lesion segmentation, limiting their ability to capture longitudinal change and excluding patients without an MRI-visible lesion.
arXiv:2606. 14724v1 Announce Type: cross Abstract: Video anomaly detection in surveillance settings must balance detection accuracy against real-time throughput, a tension that existing methods address either through stronger feature extractors or more efficient architectures, but rarely both.
By Xinze Zhang
arXiv:2607. 19696v1 Announce Type: cross Abstract: The accurate diagnosis of spinal pathologies depends heavily on radiological interpretation, yet automated systems are hindered by the lack of diverse, high-quality benchmarks.
By Duong Ngoc Vu, Hai Son Nguyen, Trong-Nghia Nguyen, Bien Tran Van, Trang Mai Xuan, Huan Vu, Thien Van Luong
arXiv:2607. 06919v1 Announce Type: cross Abstract: Artificial intelligence (AI) analysis of micro-ultrasound ($\mu$US) has shown promise for prostate cancer (PCa) detection.
By Paul F. R. Wilson, Mohamed Harmanani, Zhuoxin Guo, Obed K. Dzikunu, Hannes Cash, Adam Kinnaird, Brian Wodlinger, Purang Abolmaesumi, Parvin Mousavi