BiCLIP is a bidirectional multimodal framework that enhances medical image segmentation by allowing visual features to iteratively refine textual representations, improving semantic alignment. It incorporates an augmentation consistency objective to stabilize learning against perturbed inputs. Experiments on QaTa-COV19 and MosMedData+ show that BiCLIP outperforms state‑of‑the‑art image‑only and multimodal baselines, achieving strong performance even with only 1% labeled data and resisting common clinical artifacts such as motion blur and low‑dose CT noise.
By Saivan Talaei, Fatemeh Daneshfar, Abdulhady Abas Abdullah, Mourad Oussalah
The paper proposes a two‑stage learning framework for multi‑organ segmentation that handles partially annotated datasets and domain shifts. First, the model learns accurate segmentations from available annotations to build robust feature representations. Second, it introduces learnable organ prototypes and a Sinkhorn‑triplet loss to enforce organ‑wise feature consistency across datasets, keeping embeddings of the same organ close while separating different organs, even when annotations are missing.
By Dakini Mallam Garba, Salim Abdou Daoura
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 SSS, a semi‑supervised framework that builds on the Vision Foundation Model SAM‑2 to improve medical image segmentation. It combines a weak‑to‑strong consistency regularization with a Discriminative Feature Enhancement mechanism and a prompt generator that uses Physical Constraints with a Sliding Window to supply prompts for unlabeled data. Experiments on the ACDC and BHSD datasets show that SSS outperforms prior methods, achieving a 53.15 Dice score on BHSD, a +3.65 improvement over the state of the art.
By Hongjie Zhu, Xiwei Liu, Rundong Xue, Zeyu Zhang, Yong Xu, Daji Ergu, Ying Cai, Yang Zhao
arXiv:2609.25850v1 Announce Type: new
Abstract: Deep learning performance generally improves with increasing training data, yet this scaling is fundamentally constrained by annotation cost in large-s...
By Xiaofei Du, Lei Zhang, Shuyu Yan, Manning Wang, Zhijian Song
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:2607. 24453v1 Announce Type: cross Abstract: Learning from minimal human supervision is a long-standing goal in medical image analysis, where dense expert annotations are costly.
By Mingzhi Xu, Yizhe Zhang
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
arXiv:2605. 15720v2 Announce Type: replace-cross Abstract: Medical referring image segmentation (MRIS) predicts lesion masks from medical images and natural-language referring expressions, but acquiring paired pixel-level annotations and referring texts is costly.
By Yuchen Li, Ziru Wei, Zhen Zhao, Yi Liu, Luping Zhou
arXiv:2511.05782v3 Announce Type: replace
Abstract: Unsupervised domain adaptation (UDA) for medical image segmentation remains challenging due to substantial domain shifts across imaging modalities,...
By Lalit Maurya, Honghai Liu, Reyer Zwiggelaar
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
SAUF-Net is a semi‑supervised medical image segmentation framework that learns structure–appearance representations with uncertainty feedback. It decomposes bottleneck features into structural and appearance components, injects them into decoding, and uses auxiliary decoders and a dual‑head discriminator to estimate reliability and uncertainty. Experiments on ISIC‑2016 and Kvasir‑SEG show that SAUF‑Net surpasses state‑of‑the‑art methods, particularly when few labels are available.
By Qin Lu, Zheyang Jing, Yujie Yang, Jianwang Li, Chen Yi, Shaofeng Jiang