arXiv:2607.10851v2 Announce Type: replace
Abstract: Medical image classification models are ideally expected to identify diagnostically relevant regions while making predictions, yet standard classif...
By Tonmoy Hossain, Atiqur Rahman, Farhana Hossain Swarnali, Miaomiao Zhang
arXiv:2606. 04705v1 Announce Type: cross Abstract: Semantic segmentation in medical imaging is a critical yet challenging task due to data scarcity and high variability across modalities.
By Amirhossein Movahedisefat, Amirreza Fateh, Mohammad Reza Mohammadi
Medical image classification models are ideally expected to identify diagnostically relevant regions while making predictions, yet standard classification losses rarely provide spatial supervision. Explicit supervision via anatomical shape information, such as segmentation masks of task-relevant anatomy, has been shown to guide the network toward regions relevant to the target prediction.
GazeRefine is a training‑free framework that uses eye‑gaze data as an inference‑time prompt for zero‑shot medical image segmentation. It converts sparse, duration‑weighted fixations into foreground and background priors that initialize semantic prototypes in a frozen DINOv3 feature space, then iteratively refines these prototypes through discrimination, affinity propagation, and anchoring to the gaze guidance. The method achieves strong results on colonoscopy polyp segmentation and competitive performance on prostate MRI, demonstrating that gaze‑guided prototype refinement can enable segmentation without dense expert annotations or model fine‑tuning.
By Mohammed Oussama Benyahia, Marouane Tliba, Mohamed Amine Kerkouri, Taifour Yousra, Bin Wang, Max Bengtsson, Gorkem Durak, Elif Keles, Zuheng Ming, Marek Penhaker, Azeddine Beghdadi, Ulas Bagci, Aladine Chetouani
arXiv:2607.12896v3 Announce Type: replace
Abstract: Medical image segmentation foundation models are expected to generalize across diverse clinical scenarios, yet existing universal methods remain fr...
By Yunzhou Li, Jiesi Hu, Yanwu Yang, Hanyang Peng, Chenfei Ye, Jianfeng Cao, Yixuan Yuan, Ting Ma
arXiv:2607. 13237v1 Announce Type: cross Abstract: Precise spatial-temporal annotation of laparoscopic videos is time-consuming and requires expert knowledge.
By Manasa Dendukuri, Matjaz Jogan, Daniel A. Hashimoto, Guiqiu Liao