Jul 2026· IEEE International Conference on Consumer Electronics· pp. 679-684· 0 citations· 40 references
Abstract
Medical image segmentation is a key component of computer-aided diagnosis and treatment planning. Despite substantial progress in deep learning–based models, most existing approaches depend heavily on large annotated datasets and often fail to generalize across heterogeneous clinical environments, limiting their deployment in real-world settings characterized by domain shifts and scarce expert annotations. This paper presents a zero-shot learning framework named GroundMed-SAM for medical image segmentation. The framework integrates GroundingDINO for prompt-based region localization and MedSAM for mask generation. To address the weak alignment between visual features and medical semantics in GroundingDINO, which is pretrained on general domain image-text pairs, we introduce learnable medical text embeddings that explicitly parameterize domain-specific terminology in a continuous semantic space. These embeddings are optimized during training to better align medical concepts with visual representations, thereby strengthening text-image correspondence and improving detection-guided segmentation. The proposed framework preserves true zero-shot capability, enabling segmentation of previously unseen anatomical structures without task-specific labels. Extensive experiments on multiple public datasets across diverse modalities and clinical contexts demonstrate that our method achieves competitive segmentation performance in-domain while exhibiting superior robustness under cross-domain evaluation. Although supervised baselines outperform the proposed framework by only 3–5% on in-domain datasets, they experience substantial performance degradation when evaluated on unseen domains. Additionally, the framework achieves an AUC of 98.9 in endoscopic polyp detection, highlighting the effectiveness of the proposed medical-aware textual embeddings in guiding region localization. These results demonstrate the effectiveness of the proposed framework in improving cross-domain generalization for medical image segmentation with limited annotations.
Few-shot medical image segmentation relies on dense, boundary-sensitive prototype matching, yet common pre-training objectives mainly optimize global alignment or reconstruction, creating an objective gap that hurts boundary delineation and increases adaptation cost. This raises the question: how to pre-train representations intrinsically matchable for episodic FSS while requiring minimal adaptation-induced re-organization? We introduce a Drift-Gap diagnostic to quantify intrinsic dense-matching misalignment and adaptation-induced feature drift. Guided by this lens, we propose BOG-PRETRAIN, combining Reliability-Gated Alignment to mitigate noisy report supervision, Semantic-Guided MIM to emphasize boundary-informative regions, and Dual Consistency Regularization to stabilize episodic metric geometry. Across five benchmarks (1/4/16-shot), BOG-PRETRAIN improves mean Dice by +15.5/+15.9/+12.5 points over best priors and reduces mean HD95 by 0.9/4.6/10.1; it achieves the lowest Drift (0.052 vs. 0.155 baseline) and Gap_PT (0.256), with ablations confirming the components' complementarity.
Shou-Peng Chen, Yi-Ming Miao, Li-Mei Peng et al.· Proceedings of the Thirty-Fi...· 0 citations
The Segment Anything Model (SAM) has demonstrated groundbreaking performance in natural image segmentation, yet its direct application to medical imaging remains suboptimal due to domain shifts in data distributions and the inherent 3D nature of medical data. Although recent SAM-based methods have employed parameter-efficient transfer learning (PETL) to adapt SAM for medical tasks, they often overlook the critical 3D contextual information essential for accurate volumetric segmentation. To address this limitation, we introduce VSS-SAM++, a novel dual-branch architecture that combines SAM's foundational visual priors with Vision Mamba's capacity for modeling long-range spatial dependencies. In this framework, SAM serves as the primary encoder for high-level feature extraction, while a parallel Mamba branch captures cross-slice dependencies in 3D medical volumes. A gated hybrid attention module then dynamically fuses complementary features from both branches, adaptively weighting multi-view representations to minimize feature ambiguity and enhance segmentation precision. Extensive evaluations across nine public CT and MRI datasets demonstrate that VSS-SAM++ outperforms existing methods by 0.2-11.3% in Dice score on multi-organ and lesion segmentation tasks. The framework's robustness to domain shifts and scalability across diverse modalities highlights its potential for clinical deployment.
Jinxuan Lyu, Bin Zhang, Yipan Wang et al.· IEEE Transactions on Medical...· 0 citations
Medical image segmentation plays a pivotal role in computer-aided diagnosis. However, the scarcity of annotated data severely hinders the deployment of deep learning models. Few-shot learning (FSL) is designed to achieve rapid adaptation to unseen classes using limited labeled samples, among which prototype-based methods have emerged as a dominant paradigm. Nevertheless, existing approaches often rely on single or coarse multi-prototype representations, failing to capture complex morphological variations and local details in medical images. Furthermore, boundary feature contamination arising from convolutional receptive fields severely degrades segmentation accuracy. To address these challenges, this paper proposes an Auxiliary Supervision-guided Edge Prototype Purification Network (EPPNet) for few-shot medical image segmentation. Specifically, the network introduces a novel Prototype Purification Module (PPM). By evaluating the semantic consistency between edge and main prototypes, the PPM selectively integrates high-confidence edge prototypes via a learnable adaptive threshold, aiming to substantially alleviate boundary feature contamination. Simultaneously, an Adaptive Prototype Generation (APG) module and an Adaptive Weight Decoding (AWD) mechanism are designed to dynamically extract semantic-aware prototypes for foreground, background, and edge regions, while assigning optimal fusion weights. Furthermore, a parallel U-Net auxiliary branch is constructed to enhance the generalization capability and spatial context modeling of the feature encoder through fully supervised dense pixel prediction. Extensive experiments on three public datasets (CHAOS-MRI, Synapse-CT, and CMRSeg) demonstrate that the proposed method outperforms existing state-of-the-art techniques across multiple organ segmentation tasks. Detailed analyses and validation further confirm significant improvements in prototype representation and boundary information utilization.
Wen-Jie Meng, Kai Liu, Minghui Wang· IEEE Transactions on Medical...· 0 citations
Few-shot medical image segmentation (FS-MIS) aims to segment novel regions of interest (ROIs) from a few annotated support examples. Despite rapid progress, existing FS-MIS solutions span diverse paradigms but are evaluated under inconsistent settings, leaving their relative effectiveness unclear. We introduce FAME, a unified benchmark for evaluating FS-MIS solutions, covering specialists, SAM-based methods, CLIP-based methods, and MLLM-based methods. FAME contains 14,958 test samples across 7 anatomical sites, 9 imaging modalities, and 14 ROI categories, and evaluates models under zero-shot and ten-shot settings with additional assessment of target-absence recognition and generalization under covariate and semantic shifts. Our evaluation reveals several findings. First, effective few-shot segmentation depends on how models exploit support examples: direct visual adaptation generally outperforms prompt-based strategies. Second, increasing support examples improves performance only when models can effectively utilize them. Third, semantic transfer remains substantially more challenging than imaging-domain adaptation, and strong localization ability does not necessarily imply reliable target-absence recognition. We hope FAME provides a comprehensive understanding of current FS-MIS solutions and facilitates the development of more effective and reliable few-shot medical segmentation methods.
Jinghong Liu, Yuchuan Deng, Fanping Liu et al.· arXiv.org· 0 citations
GLNet adopts a dual-branch encoder that combines a CNN-based Local Detail Perception Branch with a Mamba-based Global Context Modeling Branch, enabling the joint extraction of fine-grained local features and long-range semantic representations.
This work presents MedPixel, a unified medical pixel-language model built around a shared language--mask interface that achieves strong performance in both pixel-level prediction and response generation, together with effective zero-shot transfer to external grounding benchmarks and robustness to imperfect spatial prompts.
Haoyu Yang, Meixing Shi, Zeng-Jie Chen et al.· 0 citations
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