Computerized Medical Imaging and Graphics最新文献

{"title":"Spectral attribute reasoning for interpretable multi-modal pathological segmentation","authors":"Lixin Zhang ,&nbsp;Qian Wang ,&nbsp;Zhao Chen ,&nbsp;Ying Chen","doi":"10.1016/j.compmedimag.2026.102707","DOIUrl":"10.1016/j.compmedimag.2026.102707","url":null,"abstract":"<div><div>Accurate segmentation of diverse histological entities is fundamental in computational pathology and critical for clinical diagnosis. Advances in microscopic imaging provide complementary information, particularly microscopic hyperspectral images (MHSIs) capture pathological differences through distinct spectral signatures, while RGB images offer high-resolution spatial and texture details. However, most multi-modal methods emphasize representation learning and modality alignment, but they offer limited insight into how the modalities interact to inform segmentation. This lack of explicit reasoning limits interpretability, and existing approaches, largely based on text prompts or spatial patterns, fail to exploit the pathology-relevant spectral signatures in MHSIs. To address these gaps, we propose Pisa-Net, a <strong>P</strong>athology-<strong>I</strong>nterpretable <strong>S</strong>pectral <strong>A</strong>ttribute Learning <strong>Net</strong>work for MHSI–RGB segmentation. Pisa-Net performs interpretable spectral reasoning through knowledge-driven attribute learning, incorporating pathology knowledge via pathologist-selected spectral signatures from key histological entities. These spectral attributes and the MHSI inputs are encoded through a frequency-domain representation into attribute embeddings and MHSI representations, whose similarities provide explicit pathology-grounded spectral evidence. The frequency components are further decomposed into low-, mid-, and high-frequency ranges and adaptively re-weighted via learned phase and magnitude, enabling the model to capture global semantics, structural patterns, and fine discriminative details. Guided by this spectral evidence, Pisa-Net integrates RGB and MHSI features through sparse spatial compression, ensuring that multi-modal fusion remains consistent with the underlying pathological reasoning. Experiments on public multi-modal pathology datasets demonstrate that Pisa-Net achieves superior segmentation performance in cells, glands, and tumors while improving interpretability by explicitly linking predictions to spectral evidence aligned with pathology knowledge.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"128 ","pages":"Article 102707"},"PeriodicalIF":4.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145979385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TGIAlign: Text-guided dual-branch bidirectional framework for cross-modal semantic alignment in medical vision-language","authors":"Wenhua Li,&nbsp;Lifang Wang,&nbsp;Min Zhao,&nbsp;Xingzhang Lü,&nbsp;Linwen Yi","doi":"10.1016/j.compmedimag.2025.102694","DOIUrl":"10.1016/j.compmedimag.2025.102694","url":null,"abstract":"<div><div>Medical image–text alignment remains challenging due to subtle lesion patterns, heterogeneous vision–language semantics, and the lack of lesion-aware guidance during visual encoding. Existing methods typically introduce textual information only after visual features have been computed, leaving early and mid-level representations insufficiently conditioned on diagnostic semantics. This limits the model’s ability to capture fine-grained abnormalities and maintain stable alignment across heterogeneous chest X-ray datasets. To address these limitations, we propose TGIAlign, a text-guided dual-branch bidirectional alignment framework that applies structured, lesion-centric cues to intermediate visual representations obtained from the frozen encoder. A large language model (LLM) is used to extract normalized, attribute-based lesion descriptions, providing consistent semantic guidance across samples. These cues are incorporated through the Text-Guided Image Feature Weighting (TGIF) module, which reweights intermediate feature outputs using similarity-derived weights, enabling multi-scale semantic conditioning without modifying the frozen backbone. To capture complementary visual cues, TGIAlign integrates multi-scale text-guided features with high-level visual representations through a Dual-Branch Bidirectional Alignment (DBBA) mechanism. Experiments on six public chest X-ray datasets demonstrate that TGIAlign achieves stable top-K retrieval and reliable text-guided lesion localization, highlighting the effectiveness of early semantic conditioning combined with dual-branch alignment for improving medical vision–language correspondence within chest X-ray settings.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"128 ","pages":"Article 102694"},"PeriodicalIF":4.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DFMFI: ultrasound breast cancer detection method based on dynamic fusion multi-scale feature interaction model","authors":"Chenbin Ma ,&nbsp;Haonan Zhang ,&nbsp;Lishuang Guo","doi":"10.1016/j.compmedimag.2026.102710","DOIUrl":"10.1016/j.compmedimag.2026.102710","url":null,"abstract":"<div><div>Ultrasound imaging has become an important method of breast cancer screening due to its non-invasive, low-cost, and ionizing radiation-free characteristics. However, the complexity and uncertainty of ultrasound images (such as speckle noise, morphological diversity of lesion areas, and inter-class similarity) pose challenges to traditional computer-aided diagnosis systems. In response to these issues, this paper proposes a Dynamic Fusion Multi-Scale Feature Interaction Model (DFMFI), specifically designed for the task of benign and malignant breast cancer detection in ultrasound imaging. DFMFI enhances the model's ability to model complex lesion features by combining dynamic feature fusion, multi-scale feature aggregation, and nonlinear dynamic interaction mechanisms. The model includes three core modules: the dynamic feature mixer uses overlapped spatial reduction attention and dynamic depth convolution to efficiently integrate global and local information; the efficient multi-scale feature aggregator captures multi-scale lesion features through a multi-branch structure; the dynamic gated feed forward network enhances the adaptability of feature flow through gating mechanisms and nonlinear reconstruction. Experimental results show that DFMFI significantly outperforms existing methods in terms of classification accuracy, robustness, and computational efficiency, providing an efficient and robust solution for the early screening and diagnosis of breast cancer.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"128 ","pages":"Article 102710"},"PeriodicalIF":4.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fuzzy rough set loss for deep learning-based precise medical image segmentation","authors":"Mohsin Furkh Dar ,&nbsp;Avatharam Ganivada","doi":"10.1016/j.compmedimag.2026.102716","DOIUrl":"10.1016/j.compmedimag.2026.102716","url":null,"abstract":"<div><div>Accurate segmentation of medical images is crucial for diagnosis and treatment planning, yet it remains challenging due to ambiguous lesion boundaries, class imbalance, and complex anatomical structures. We propose a novel Fuzzy Rough Set-inspired (FRS) loss function that addresses these challenges by integrating pixels’ fuzzy similarity relations with a boundary uncertainty model in a convex combination method. To obtain the boundary uncertainty model, the fuzzy lower and upper approximations of a set of pixels and membership weights are utilized. The FRS loss function enhances boundary sensitivity and handles prediction uncertainty through its dual components: a fuzzy similarity term that captures gradual transitions at lesion boundaries, and boundary uncertainty model that deals with uncertainty and mitigates class imbalance. Extensive experiments across five diverse medical imaging datasets — breast ultrasound, gastrointestinal polyps, brain Magnetic Resonance Imaging (MRI), chest Computed Tomography (CT), and skin lesions — demonstrate the effectiveness of our approach. The FRS loss achieves superior segmentation performance with an average improvement of 2.1% in Dice score compared to the best baseline method, while demonstrating statistically significant improvements across all evaluated metrics (p <span><math><mo>&lt;</mo></math></span> 0.001). The FRS loss shows its robustness to moderate class imbalance while maintaining computational efficiency (mean inference time 0.075–0.12 s per image, 4.5 MB memory). These results suggest that the FRS loss function provides a robust and interpretable framework for precise medical image segmentation, particularly in cases with ambiguous boundaries and moderate imbalance. Code: <span><span>https://github.com/MohsinFurkh/Fuzzy-Rough-Set-Loss</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"128 ","pages":"Article 102716"},"PeriodicalIF":4.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Semi-supervised medical image classification via feature-level multi-scale consistency and adversarial training","authors":"Li Shiyan,&nbsp;Wang Shuqin,&nbsp;Gu Xin,&nbsp;Sun Debing","doi":"10.1016/j.compmedimag.2025.102695","DOIUrl":"10.1016/j.compmedimag.2025.102695","url":null,"abstract":"<div><div>In recent years, semi-supervised learning (SSL) has attracted increasing attention in medical image analysis, showing great potential in scenarios with limited annotations. However, existing consistency regularization methods suffer from several limitations: overly uniform constraints at the output layer, lack of interaction within adversarial strategies, and reliance on external sample pools for sample estimation, which together lead to insufficient use of feature-level information and unstable training. To address these challenges, this paper proposes a novel semi-supervised framework, termed Feature-level multi-scale Consistency and Adversarial Training (FCAT). A multi-scale feature-level consistency mechanism is introduced to capture hierarchical structural representations through cross-level feature fusion, enabling robust feature alignment without relying on external sample pools. To overcome the limitation of unidirectional adversarial training, a bidirectional feature perturbation strategy is designed under a teacher–student collaboration scheme, where both models generate perturbations from their own gradients and enforce mutual consistency. In addition, an intrinsic evaluation mechanism based on entropy and complementary confidence is developed to rank unlabeled samples according to their information content, guiding the training process toward informative hard samples while reducing overfitting to trivial ones. Experiments on the balanced Pneumonia Chest X-ray and NCT-CRC-HE histopathology datasets, as well as the imbalanced ISIC 2019 dermoscopic skin lesion dataset, demonstrate that our FCAT achieves competitive performance and strong generalization across diverse imaging modalities and data distributions.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"128 ","pages":"Article 102695"},"PeriodicalIF":4.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GenoPath-MCA: Multimodal masked cross-attention between genomics and pathology for survival prediction","authors":"Kaixuan Zhang ,&nbsp;Shuqi Dong ,&nbsp;Peifeng Shi ,&nbsp;Dingcan Hu ,&nbsp;Geng Gao ,&nbsp;Jinlin Yang ,&nbsp;Tao Gan ,&nbsp;Nini Rao","doi":"10.1016/j.compmedimag.2026.102699","DOIUrl":"10.1016/j.compmedimag.2026.102699","url":null,"abstract":"<div><div>Survival prediction using whole slide images (WSIs) and bulk genes is a key task in computational pathology, essential for automated risk assessment and personalized treatment planning. While integrating WSIs with genomic features presents challenges due to inconsistent modality granularity, semantic disparity, and the lack of personalized fusion. We propose <strong>GenoPath-MCA</strong>, a novel multimodal framework that models dense cross-modal interactions between histopathology and gene expression data. A masked co-attention mechanism aligns features across modalities, and the Multimodal Masked Cross-Attention Module (<strong>M2CAM</strong>) jointly captures high-order image–gene and gene–gene relationships for enhanced semantic fusion. To address patient-level heterogeneity, we develop a Dynamic Modality Weight Adjustment Strategy (<strong>DMWAS</strong>) that adaptively modulates fusion weights based on the discriminative relevance of each modality. Additionally, an importance-guided patch selection strategy effectively filters redundant visual inputs, reducing computational cost while preserving critical context. Experiments on public multimodal cancer survival datasets demonstrate that GenoPath-MCA significantly outperforms existing methods in terms of concordance index and robustness. Visualizations of multimodal attention maps validate the biological interpretability and clinical potential of our approach.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"128 ","pages":"Article 102699"},"PeriodicalIF":4.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145928057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unified model with random penalty entropy loss for robust nasogastric tube placement analysis in X-ray","authors":"GwiSeong Moon ,&nbsp;Kyoung Min Moon ,&nbsp;Inseo Park ,&nbsp;Kanghee Lee ,&nbsp;Doohee Lee ,&nbsp;Woo Jin Kim ,&nbsp;Yoon Kim ,&nbsp;Ji Young Hong ,&nbsp;Hyun-Soo Choi","doi":"10.1016/j.compmedimag.2026.102715","DOIUrl":"10.1016/j.compmedimag.2026.102715","url":null,"abstract":"<div><h3>Background and objective:</h3><div>An accurate nasogastric (NG) tube placement assessment is essential to prevent serious complications. However, manual chest X-ray verification is prone to human error and variability. We propose a unified deep learning model that jointly performs segmentation and classification to improve the generalization and reliability of automated NG tube placement assessment.</div></div><div><h3>Methods:</h3><div>We developed a unified architecture based on nnUNet, which was optimized simultaneously for segmentation and classification. To enhance robustness and reduce overconfidence, we introduce Random Penalty Entropy Loss, which dynamically scales entropy penalties during training. The model was evaluated on internal datasets (5674 chest X-rays from three South Korean hospitals) and an external dataset from MIMIC-CXR.</div></div><div><h3>Results:</h3><div>On the internal test set, the proposed model outperformed the Wang 2-Stage method (F1: 93.94% vs. 87.39%), particularly in ambiguous cases. Baseline models using Focal Loss or Label Smoothing performed well internally but showed substantial performance drops and miscalibration externally. In contrast, our model with Random Penalty Entropy Loss achieved the highest external classification accuracy (F1: 66.34%, AUROC: 84.82%) and superior calibration (MCE: 0.429, ECE: 0.274).</div></div><div><h3>Conclusion:</h3><div>The proposed unified model surpasses existing two-stage approaches in classification and calibration. Incorporating Random Penalty Entropy Loss improves robustness and generalization across diverse clinical settings. These results highlight the model’s potential to reduce diagnostic errors and enhance patient safety in NG tube placement assessment.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"128 ","pages":"Article 102715"},"PeriodicalIF":4.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146120982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A hybrid Transformer-CNN framework for uncertainty-guided semi-supervised multiclass eye disease classification with enhanced interpretability","authors":"Muhammad Hammad Malik ,&nbsp;Zishuo Wan ,&nbsp;Yingying Ren ,&nbsp;Da-Wei Ding","doi":"10.1016/j.compmedimag.2026.102701","DOIUrl":"10.1016/j.compmedimag.2026.102701","url":null,"abstract":"<div><div>The accurate classification of eye diseases such as cataract, diabetic retinopathy (DR), glaucoma, and healthy conditions from fundus images remains a critical challenge in ophthalmology, requiring early diagnosis and treatment to prevent vision loss. Existing deep learning methods rely on large labeled datasets, inefficient use of unlabeled data, and limited interpretability, restricting clinical applicability. To address these limitations, we propose a novel CNN-Transformer hybrid architecture coupled with innovative semi-supervised learning (SSL) and explainability techniques to enhance multiclass eye disease classification. Our methodology integrates a ConvNeXt backbone with Transformer modules, leveraging multi-head attention to effectively capture both spatial features and long-range dependencies. We introduce Uncertainty-Guided MixMatch (UG-MixMatch), a semi-supervised framework that leverages Monte Carlo (MC) dropout for uncertainty quantification and pseudo-label refinement, effectively utilizing both labeled and unlabeled data. For interpretability, we propose a novel Gradient-based Integrated Attention Map (GIAM), which aggregates attention maps across multiple layers. It incorporates adaptive channel-wise weighting, offering more detailed insights into model predictions, surpassing traditional Grad-CAM methods. Evaluated on the Ocular Imaging Health (OIH) dataset of 4215 fundus images across four classes, our approach achieved a 95.27 % classification accuracy using UG-MixMatch and 95.51 % when incorporating MC dropout for direct model evaluation. Cohen’s kappa score reached 93.70, indicating near-perfect agreement with the ground truth. Class-wise performance was exceptional, with 100 % sensitivity and specificity for DR and over 95 % specificity for cataract and glaucoma. Robust AUC values were observed, including 1.00 for DR and cataract and 0.99 for glaucoma and healthy cases. GIAM visualizations effectively highlighted disease-relevant regions, offering enhanced clinical interpretability and validation potential. Our framework addresses data scarcity, enhances interpretability, and delivers clinically relevant performance, a promising step towards scalable, explainable, and accurate diagnostic tools for Clinical Decision Support Systems (CDSS) and ophthalmic screening.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"128 ","pages":"Article 102701"},"PeriodicalIF":4.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145928055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Initial evaluation of a mixed-reality system for image-guided navigation during percutaneous liver tumor ablation","authors":"Dominik Spinczyk ,&nbsp;Grzegorz Rosiak ,&nbsp;Jarosław Żyłkowski ,&nbsp;Krzysztof Milczarek ,&nbsp;Dariusz Konecki ,&nbsp;Karol Zaczkowski ,&nbsp;Agata Tomaszewska ,&nbsp;Łukasz Przepióra ,&nbsp;Anna Wolińska-Sołtys ,&nbsp;Piotr Sperka ,&nbsp;Dawid Hajda ,&nbsp;Ewa Piętka","doi":"10.1016/j.compmedimag.2026.102714","DOIUrl":"10.1016/j.compmedimag.2026.102714","url":null,"abstract":"<div><div>Minimally invasive ablation is a challenge for contemporary interventional radiology. This study aimed to investigate the feasibility of utilizing a mixed-reality system for this type of treatment. A HoloLens mixed reality and optical tracking system, which supports diagnosis, planning, and procedure implementation, was used for percutaneous liver tumor ablation. The system differentiated pathological liver changes at the diagnostic stage, allowing for the selection of the entry point and target during planning. Meanwhile, it provided a real-time fusion of intraoperative ultrasound images with a pre-operative hologram during the procedure. Additionally, the collision detection module enabled the detection of collisions between the ablative needle and anatomical structures, utilizing the actual needle trajectory. The system was evaluated in 11 patients with cancerous liver lesions. The mean accuracy of target point registration, selected at the planning stage, was 2.8 mm during the procedure's supporting stage. Additionally, operator depth perception improved, the effective needle trajectory was shortened, and the radiation dose was reduced for both the patient and the operator due to improved visibility of the needle within the patient’s body. A generally improved understanding of the mutual spatial relationship between anatomical structures was observed compared to the classical two-dimensional view, along with improved depth perception of the operating field. An additional advantage indicated by the operators was the real-time highlighting of anatomical structures susceptible to damage by the needle trajectory, such as blood vessels, bile ducts, and the lungs, which lowers the risk of complications.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"128 ","pages":"Article 102714"},"PeriodicalIF":4.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AutoPromptSeg: Automated Decoupling of Uncertainty Prompts with SAM for semi-supervised medical image segmentation","authors":"Junan Zhu,&nbsp;Zhizhe Tang,&nbsp;Ping Ma,&nbsp;Zheng Liang,&nbsp;Chuanjian Wang","doi":"10.1016/j.compmedimag.2026.102708","DOIUrl":"10.1016/j.compmedimag.2026.102708","url":null,"abstract":"<div><div>The scarcity of high-quality annotated data limits the application of supervised learning in disease diagnosis. Semi-supervised learning (SSL) offers a promising solution to this challenge, utilizing both limited labeled data and large-scale unlabeled data to significantly boost segmentation accuracy. While existing SSL methods focus on model-centric regularization strategies, the emergence of promptable foundation models like Segment Anything (SAM) presents new opportunities for paradigmatic advancement. However, SAM requires datasets with additional prompt annotations to guide the segmentation process, yet most existing medical imaging datasets do not contain them. To address this limitation, we introduce a novel semi-supervised 3D medical image segmentation method called AutoPromptSeg, which generates effective prompts with the Decoupled Uncertainty Prompt Generator (DUPG) while maintaining superior segmentation performance in data-scarce scenarios. Concurrently, we employ the Channel Alignment and Fusion Architecture (CAFA) to align features obtained from different branches, thereby bolstering the representational capacity of unlabeled data. Our proposed approach achieves state-of-the-art performance on three benchmarks: the multi-modality abdominal multi-organ segmentation challenge 2022 dataset (Amos 2022), the left atrium dataset (LA), and the brain tumor segmentation challenge 2020 dataset (BraTS 2020). AutoPromptSeg achieves Dice Score of 68.78% on Amos 2022, 90.02% on LA, and 86.63% on BraTS 2020 under only 10% labeled data setting, demonstrating the excellent performance of our semi-supervised learning framework in limited annotated data.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"128 ","pages":"Article 102708"},"PeriodicalIF":4.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}