arXiv:2505.03380v2 Announce Type: replace
Abstract: Accurate delineation of tumors and surrounding organs-at-risk is essential for radiotherapy, surgery and treatment response assessment, yet remains...
By Haonan Wang, Jiaji Mao, Lehan Wang, Qixiang Zhang, Marawan Elbatel, Yi Qin, Huijun Hu, Baoxun Li, Wenhui Deng, Weifeng Qin, Hongrui Li, Jialin Liang, Jun Shen, Xiaomeng Li
The paper introduces Spatial‑FAD, a few‑shot medical anomaly detection framework that fuses Vision‑Language Model (CLIP) semantics with spatial priors from Vision Foundation Models (DINO). A VFM‑enhanced adapter injects structural affinity into CLIP features, while a sliding‑window aggregation produces high‑resolution embeddings for finer lesion localization. Prototype‑enhanced support memory further improves efficiency and performance, yielding significant gains on Liver CT, Retinal OCT, and Brain MRI datasets, notably an 11.4% Dice improvement in 4‑shot scenarios.
By Juzheng Miao, Yuchen Yuan, Cheng Chen, Pheng-Ann Heng
FoRIS is a training‑free in‑context segmentation framework that refines foreground masks through a coarse‑to‑fine process. It operates in three stages—Foreground Purification, Localization, and Consolidation—to suppress background noise, pinpoint target regions, and reconstruct complete foreground structures. The method achieves state‑of‑the‑art performance, improving mIoU by 4.5 and 4.8 points in 1‑shot and 5‑shot settings respectively.
By Ming Hu, Jianfu Yin, Mingyu Dou, Miaomiao Zhang, Yao Wang, Cong Hu, Bingliang Hu, Quan Wang
SEG-SAM is a unified medical image segmentation model that builds on the Segment Anything Model (SAM) by integrating semantic medical knowledge. It introduces a semantic‑aware decoder separate from SAM’s original decoder to handle both semantic segmentation of prompted objects and classification of unprompted objects. The model also incorporates key medical category characteristics from large language models via a text‑to‑vision semantic module and uses a cross‑mask spatial alignment strategy to improve overlap between predictions, achieving superior performance over existing SAM‑based and task‑specific methods.
By Shuangping Huang, Hao Liang, Qingfeng Wang, Chulong Zhong, Zijian Zhou, Miaojing Shi
The paper introduces DPIF, a plug‑and‑play dual‑path interaction framework that can be integrated with any encoder‑decoder segmentation network. DPIF aligns cross‑view distributions using Sinkhorn optimal transport, decouples features via conditional mutual information to transfer knowledge within shared semantic spaces, and applies persistent homology‑based topological regularization to encode anatomical invariants. Experiments on a private echocardiography dataset and two public datasets show that DPIF‑enhanced models outperform single‑view baselines and achieve comparable performance to full‑data single‑view models while using only 40–60% of the data.
By Shuhuan Wang, Yuanyuan Cui, Pengju Wang, Huimin Guo, Ling Yue, Yin Gu, Yanchao Zhang, Wei Feng, Yunhe Liu, Wei Qian, Mingjun Xu, Shuang Liu, He Ma
Recent advances in semi-supervised medical image segmentation have achieved remarkable performance through prediction consistency, pseudo-label supervision, and hard-region supervision. However, these methods primarily improve supervision quality rather than explicitly enforcing semantic consistency in the learned representations of hard regions.