G^2RA‑Net is a medical image segmentation framework that models cross‑slice relations using a graph‑based approach and an attention gating mechanism. It represents each image slice as a graph node, propagates semantic context through graph message passing, and then applies a Cross‑Slice Attention Gate to select relevant neighboring context and emphasize target anatomical regions. Experiments on brain MRI and abdominal CT datasets show that G^2RA‑Net improves segmentation accuracy and boundary quality compared to representative methods, with ablation studies confirming the effectiveness of its design.
arXiv:2606. 16153v1 Announce Type: cross Abstract: Medical image segmentation plays a critical role in clinical diagnostics, treatment planning, disease monitoring, and neurological disorder identification.
By Pengyu Zhu, Xiaojing Zhang, Kunbo Zhang, Chunyan Zhang, Zhenyu Wang
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:2608. 08575v1 Announce Type: cross Abstract: Accurate 3D medical image segmentation requires the integration of long-range anatomical context with fine boundary detail.
By Zheyang Jing, Qin Lu, Jianwang Li, Yujie Yang, Chen Yi, Shaofeng Jiang
The paper introduces MSM‑Seg, a dual‑memory segmentation framework for 3D multi‑modal brain tumor segmentation. It combines a modality‑and‑slice memory attention module to capture cross‑modal and spatial‑slice dependencies, a multi‑scale category‑agnostic prompt encoder for whole‑tumor guidance, and a modality‑adaptive fusion decoder to integrate complementary decoding information. Experiments on various MRI datasets show that MSM‑Seg surpasses state‑of‑the‑art methods for metastases and glioma tumor segmentation.
By Yuxiang Luo, Qing Xu, Hai Huang, Yuqi Ouyang, Xiangjian He, Zhen Chen, Wenting Duan, Jiebo Luo
arXiv:2602.20773v2 Announce Type: replace
Abstract: Purpose: Developing generalizable medical image segmentation models is challenging because imaging data are distributed across institutions and dif...
By Sachin Dudda Nagaraju, Ashkan Moradi, Bendik Skarre Abrahamsen, Mattijs Elschot
arXiv:2607. 14328v1 Announce Type: cross Abstract: In proton therapy planning, respiratory-gated non-contrast CT (NCCT) is commonly used for lesion segmentation; however, accurate delineation remains challenging due to low lesion-to-background contrast.
By San Lee, Nalee Kim, Jeong Il Yu, Hee Chul Park, Boah Kim
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.
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
The paper introduces GAD-MambaUNet, a lightweight medical image segmentation network that integrates efficient local modeling, Direction-Group Graph Selective Scan (DG‑GSS) for structured information exchange, and training‑time supervision from a frozen DINOv3 teacher with Gradient‑Adaptive Distillation. GAD‑MambaUNet demonstrates a strong accuracy‑efficiency trade‑off compared to other lightweight and general segmentation methods, and ablation studies confirm the benefits of DG‑GSS and DINOv3‑GAD supervision. Future work aims to refine teacher‑student alignment and apply the framework to multi‑class and multi‑modal medical segmentation tasks.
By Fang Wang, Huitao Li, Wenhan Chao, Zheng Zhuo, Xinxin Yang
arXiv:2608.22619v1 Announce Type: cross
Abstract: Generative segmentation provides an alternative to direct pixel-wise prediction by operating on learned latent representations, but effective image-t...
By Md Maklachur Rahman, Md Hasan Al Banna, Saraf Anjum, Mahmudul Hasan, Tracy Hammond
The paper introduces LowBridge, a method for cross‑modal medical image segmentation that leverages shared low‑level features such as edges between MRI and CT scans. It trains a generative model to reconstruct source‑modality images from edge maps and then trains a segmentation network on these generated images. At test time, edge features from target‑modality images are fed into the generative model to produce source‑style images, which are segmented by the pretrained network, achieving state‑of‑the‑art results on multiple public datasets.
By Pengfei Lyu, Pak-Hei Yeung, Jing Xia, De Hu, Xiaosheng Yu, Jianning Chi, Chengdong Wu, Jagath C. Rajapakse