Medical image analysis最新文献

{"title":"MMR-Mamba: Multi-modal MRI reconstruction with Mamba and spatial-frequency information fusion","authors":"Jing Zou ,&nbsp;Lanqing Liu ,&nbsp;Qi Chen ,&nbsp;Shujun Wang ,&nbsp;Zhanli Hu ,&nbsp;Xiaohan Xing ,&nbsp;Jing Qin","doi":"10.1016/j.media.2025.103549","DOIUrl":"10.1016/j.media.2025.103549","url":null,"abstract":"<div><div>Multi-modal MRI offers valuable complementary information for diagnosis and treatment; however, its clinical utility is limited by prolonged scanning time. To accelerate the acquisition process, a practical approach is to reconstruct images of the target modality, which requires longer scanning time, from under-sampled k-space data using the fully-sampled reference modality with shorter scanning time as guidance. The primary challenge of this task lies in comprehensively and efficiently integrating complementary information from different modalities to achieve high-quality reconstruction. Existing methods struggle with this challenge: (1) convolution-based models fail to capture long-range dependencies; (2) transformer-based models, while excelling in global feature modeling, suffer from quadratic computational complexity. To address this dilemma, we propose MMR-Mamba, a novel framework that thoroughly and efficiently integrates multi-modal features for MRI reconstruction, leveraging Mamba’s capability to capture long-range dependencies with linear computational complexity while exploiting global properties of the Fourier domain. Specifically, we first design a <em>Target modality-guided Cross Mamba</em> (TCM) module in the spatial domain, which maximally restores the target modality information by selectively incorporating relevant information from the reference modality. Then, we introduce a <em>Selective Frequency Fusion</em> (SFF) module to efficiently integrate global information in the Fourier domain and recover high-frequency signals for the reconstruction of structural details. Furthermore, we devise an <em>Adaptive Spatial-Frequency Fusion</em> (ASFF) module, which mutually enhances the spatial and frequency domains by supplementing less informative channels from one domain with corresponding channels from the other. Extensive experiments on the BraTS and fastMRI knee datasets demonstrate the superiority of our MMR-Mamba over state-of-the-art reconstruction methods. The code is publicly available at <span><span>https://github.com/zoujing925/MMR-Mamba</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"102 ","pages":"Article 103549"},"PeriodicalIF":10.7,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143675569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Flip Learning: Weakly supervised erase to segment nodules in breast ultrasound","authors":"Yuhao Huang ,&nbsp;Ao Chang ,&nbsp;Haoran Dou ,&nbsp;Xing Tao ,&nbsp;Xinrui Zhou ,&nbsp;Yan Cao ,&nbsp;Ruobing Huang ,&nbsp;Alejandro F. Frangi ,&nbsp;Lingyun Bao ,&nbsp;Xin Yang ,&nbsp;Dong Ni","doi":"10.1016/j.media.2025.103552","DOIUrl":"10.1016/j.media.2025.103552","url":null,"abstract":"<div><div>Accurate segmentation of nodules in both 2D breast ultrasound (BUS) and 3D automated breast ultrasound (ABUS) is crucial for clinical diagnosis and treatment planning. Therefore, developing an automated system for nodule segmentation can enhance user independence and expedite clinical analysis. Unlike fully-supervised learning, weakly-supervised segmentation (WSS) can streamline the laborious and intricate annotation process. However, current WSS methods face challenges in achieving precise nodule segmentation, as many of them depend on inaccurate activation maps or inefficient pseudo-mask generation algorithms. In this study, we introduce a novel multi-agent reinforcement learning-based WSS framework called Flip Learning, which relies solely on 2D/3D boxes for accurate segmentation. Specifically, multiple agents are employed to erase the target from the box to facilitate classification tag flipping, with the erased region serving as the predicted segmentation mask. The key contributions of this research are as follows: (1) Adoption of a superpixel/supervoxel-based approach to encode the standardized environment, capturing boundary priors and expediting the learning process. (2) Introduction of three meticulously designed rewards, comprising a classification score reward and two intensity distribution rewards, to steer the agents’ erasing process precisely, thereby avoiding both under- and over-segmentation. (3) Implementation of a progressive curriculum learning strategy to enable agents to interact with the environment in a progressively challenging manner, thereby enhancing learning efficiency. Extensively validated on the large in-house BUS and ABUS datasets, our Flip Learning method outperforms state-of-the-art WSS methods and foundation models, and achieves comparable performance as fully-supervised learning algorithms.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"102 ","pages":"Article 103552"},"PeriodicalIF":10.7,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated planning of mandible reconstruction with fibula free flap based on shape completion and morphometric descriptors","authors":"Yan Guo ,&nbsp;Chenyao Li ,&nbsp;Rong Yang ,&nbsp;Puxun Tu ,&nbsp;Bolun Zeng ,&nbsp;Jiannan Liu ,&nbsp;Tong Ji ,&nbsp;Chenping Zhang ,&nbsp;Xiaojun Chen","doi":"10.1016/j.media.2025.103544","DOIUrl":"10.1016/j.media.2025.103544","url":null,"abstract":"<div><div>Vascularized fibula free flap (FFF) grafts are frequently used to reconstruct mandibular defects. However, the current planning methods for osteotomy, splicing, and fibula placement present challenges in achieving satisfactory facial aesthetics and restoring the original morphology of the mandible. In this study, we propose a novel two-step framework for automated preoperative planning in FFF mandibular reconstruction. The framework is based on mandibular shape completion and morphometric descriptors. Firstly, we utilize a 3D generative model to estimate the entire mandibular geometry by incorporating shape priors and accounting for partial defect mandibles. Accurately predicting the premorbid morphology of the mandible is crucial for determining the surgical plan. Secondly, we introduce new two-dimensional morphometric descriptors to assess the quantitative difference between the planning scheme and the full morphology of the mandible. We have designed intuitive and valid variables specifically designed to describe the planning scheme and constructed an objective function to measure the difference. By optimizing this function, we can achieve the best shape-matched 3D planning solution. Through a retrospective study involving 65 real tumor patients, our method has exhibited favorable results in both qualitative and quantitative analyses when compared to the planned results of experienced clinicians using existing methods. This demonstrates that our method can implement an automated preoperative planning technique, eliminating subjectivity and achieving user-independent results. Furthermore, we have presented the potential of our automated planning process in a clinical case, highlighting its applicability in clinical settings.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"102 ","pages":"Article 103544"},"PeriodicalIF":10.7,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143685374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An extragradient and noise-tuning adaptive iterative network for diffusion MRI-based microstructural estimation","authors":"Tianshu Zheng ,&nbsp;Chuyang Ye ,&nbsp;Zhaopeng Cui ,&nbsp;Hui Zhang ,&nbsp;Daniel C. Alexander ,&nbsp;Dan Wu","doi":"10.1016/j.media.2025.103535","DOIUrl":"10.1016/j.media.2025.103535","url":null,"abstract":"<div><div>Diffusion MRI (dMRI) is a powerful technique for investigating tissue microstructure properties. However, advanced dMRI models are typically complex and nonlinear, requiring a large number of acquisitions in the <em>q</em>-space. Deep learning techniques, specifically optimization-based networks, have been proposed to improve the model fitting with limited <em>q</em>-space data. Previous optimization procedures relied on the empirical selection of iteration block numbers and the network structures were based on the <em>iterative hard thresholding</em> (IHT) algorithm, which may suffer from instability during sparse reconstruction. In this study, we introduced an <em>extragradient and noise-tuning adaptive iterative network</em>, a generic network for estimating dMRI model parameters. We proposed an adaptive mechanism that flexibly adjusts the sparse representation process, depending on specific dMRI models, datasets, and downsampling strategies, avoiding manual selection and accelerating inference. In addition, we proposed a noise-tuning module to assist the network in escaping from local minimum/saddle points. The network also included an additional projection of the extragradient to ensure its convergence. We evaluated the performance of the proposed network on the <em>neurite orientation dispersion and density imaging</em> (NODDI) model and <em>diffusion basis spectrum imaging</em> (DBSI) model on two 3T <em>Human Connectome Project</em> (HCP) datasets and a 7T HCP dataset with six different downsampling strategies. The proposed framework demonstrated superior accuracy and generalizability compared to other state-of-the-art microstructural estimation algorithms.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"102 ","pages":"Article 103535"},"PeriodicalIF":10.7,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"5D image reconstruction exploiting space-motion-echo sparsity for accelerated free-breathing quantitative liver MRI","authors":"MungSoo Kang ,&nbsp;Ricardo Otazo ,&nbsp;Gerald Behr ,&nbsp;Youngwook Kee","doi":"10.1016/j.media.2025.103532","DOIUrl":"10.1016/j.media.2025.103532","url":null,"abstract":"<div><div>Recent advances in 3D non-Cartesian multi-echo gradient-echo (mGRE) imaging and compressed sensing (CS)-based 4D (3D image space + 1D respiratory motion) motion-resolved image reconstruction, which applies temporal total variation to the respiratory motion dimension, have enabled free-breathing liver tissue MR parameter mapping. This technology now allows for robust reconstruction of high-resolution proton density fat fraction (PDFF), R<span><math><msubsup><mrow></mrow><mrow><mn>2</mn></mrow><mrow><mo>∗</mo></mrow></msubsup></math></span>, and quantitative susceptibility mapping (QSM), previously unattainable with conventional Cartesian mGRE imaging. However, long scan times remain a persistent challenge in free-breathing 3D non-Cartesian mGRE imaging. Recognizing that the underlying dimension of the imaging data is essentially 5D (4D + 1D echo signal evolution), we propose a CS-based 5D motion-resolved mGRE image reconstruction method to further accelerate the acquisition. Our approach integrates discrete wavelet transforms along the echo and spatial dimensions into a CS-based reconstruction model and devises a solution algorithm capable of handling such a 5D complex-valued array. Through phantom and in vivo human subject studies, we evaluated the effectiveness of leveraging unexplored correlations by comparing the proposed 5D reconstruction with the 4D reconstruction (i.e., motion-resolved reconstruction with temporal total variation) across a wide range of acceleration factors. The 5D reconstruction produced more reliable and consistent measurements of PDFF, R<span><math><msubsup><mrow></mrow><mrow><mn>2</mn></mrow><mrow><mo>∗</mo></mrow></msubsup></math></span>, and QSM compared to the 4D reconstruction. In conclusion, the proposed 5D motion-resolved image reconstruction demonstrates the feasibility of achieving accelerated, reliable, and free-breathing liver mGRE imaging for the measurement of PDFF, R<span><math><msubsup><mrow></mrow><mrow><mn>2</mn></mrow><mrow><mo>∗</mo></mrow></msubsup></math></span>, and QSM.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"102 ","pages":"Article 103532"},"PeriodicalIF":10.7,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143675551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Medical SAM adapter: Adapting segment anything model for medical image segmentation","authors":"Junde Wu ,&nbsp;Ziyue Wang ,&nbsp;Mingxuan Hong ,&nbsp;Wei Ji ,&nbsp;Huazhu Fu ,&nbsp;Yanwu Xu ,&nbsp;Min Xu ,&nbsp;Yueming Jin","doi":"10.1016/j.media.2025.103547","DOIUrl":"10.1016/j.media.2025.103547","url":null,"abstract":"<div><div>The Segment Anything Model (SAM) has recently gained popularity in the field of image segmentation due to its impressive capabilities in various segmentation tasks and its prompt-based interface. However, recent studies and individual experiments have shown that SAM underperforms in medical image segmentation due to the lack of medical-specific knowledge. This raises the question of how to enhance SAM’s segmentation capability for medical images. We propose the Medical SAM Adapter (Med-SA), which is one of the first methods to integrate SAM into medical image segmentation. Med-SA uses a light yet effective adaptation technique instead of fine-tuning the SAM model, incorporating domain-specific medical knowledge into the segmentation model. We also propose Space-Depth Transpose (SD-Trans) to adapt 2D SAM to 3D medical images and Hyper-Prompting Adapter (HyP-Adpt) to achieve prompt-conditioned adaptation. Comprehensive evaluation experiments on 17 medical image segmentation tasks across various modalities demonstrate the superior performance of Med-SA while updating only 2% of the SAM parameters (13M). Our code is released at <span><span>https://github.com/KidsWithTokens/Medical-SAM-Adapter</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"102 ","pages":"Article 103547"},"PeriodicalIF":10.7,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143675535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RED: Residual estimation diffusion for low-dose PET sinogram reconstruction","authors":"Xingyu Ai ,&nbsp;Bin Huang ,&nbsp;Fang Chen ,&nbsp;Liu Shi ,&nbsp;Binxuan Li ,&nbsp;Shaoyu Wang ,&nbsp;Qiegen Liu","doi":"10.1016/j.media.2025.103558","DOIUrl":"10.1016/j.media.2025.103558","url":null,"abstract":"<div><div>Recent advances in diffusion models have demonstrated exceptional performance in generative tasks across various fields. In positron emission tomography (PET), the reduction in tracer dose leads to information loss in sinograms. Using diffusion models to reconstruct missing information can improve imaging quality. Traditional diffusion models effectively use Gaussian noise for image reconstructions. However, in low-dose PET reconstruction, Gaussian noise can worsen the already sparse data by introducing artifacts and inconsistencies. To address this issue, we propose a diffusion model named residual estimation diffusion (RED). From the perspective of diffusion mechanism, RED uses the residual between sinograms to replace Gaussian noise in diffusion process, respectively sets the low-dose and full-dose sinograms as the starting point and endpoint of reconstruction. This mechanism helps preserve the original information in the low-dose sinogram, thereby enhancing reconstruction reliability. From the perspective of data consistency, RED introduces a drift correction strategy to reduce accumulated prediction errors during the reverse process. Calibrating the intermediate results of reverse iterations helps maintain the data consistency and enhances the stability of reconstruction process. In the experiments, RED achieved the best performance across all metrics. Specifically, the PSNR metric showed improvements of 2.75, 5.45, and 8.08 dB in DRF4, 20, and 100 respectively, compared to traditional methods. The code is available at: <span><span>https://github.com/yqx7150/RED</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"102 ","pages":"Article 103558"},"PeriodicalIF":10.7,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143675552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Segment Any Tissue: One-shot reference guided training-free automatic point prompting for medical image segmentation","authors":"Xueyu Liu ,&nbsp;Guangze Shi ,&nbsp;Rui Wang ,&nbsp;Yexin Lai ,&nbsp;Jianan Zhang ,&nbsp;Weixia Han ,&nbsp;Min Lei ,&nbsp;Ming Li ,&nbsp;Xiaoshuang Zhou ,&nbsp;Yongfei Wu ,&nbsp;Chen Wang ,&nbsp;Wen Zheng","doi":"10.1016/j.media.2025.103550","DOIUrl":"10.1016/j.media.2025.103550","url":null,"abstract":"<div><div>Medical image segmentation frequently encounters high annotation costs and challenges in task adaptation. While visual foundation models have shown promise in natural image segmentation, automatically generating high-quality prompts for class-agnostic segmentation of medical images remains a significant practical challenge. To address these challenges, we present Segment Any Tissue (SAT), an innovative, training-free framework designed to automatically prompt the class-agnostic visual foundation model for the segmentation of medical images with only a one-shot reference. SAT leverages the robust feature-matching capabilities of a pretrained foundation model to construct distance metrics in the feature space. By integrating these with distance metrics in the physical space, SAT establishes a dual-space cyclic prompt engineering approach for automatic prompt generation, optimization, and evaluation. Subsequently, SAT utilizes a class-agnostic foundation segmentation model with the generated prompt scheme to obtain segmentation results. Additionally, we extend the one-shot framework by incorporating multiple reference images to construct an ensemble SAT, further enhancing segmentation performance. SAT has been validated on six public and private medical segmentation tasks, capturing both macroscopic and microscopic perspectives across multiple dimensions. In the ablation experiments, automatic prompt selection enabled SAT to effectively handle tissues of various sizes, while also validating the effectiveness of each component. The comparative experiments show that SAT is comparable to, or even exceeds, some fully supervised methods. It also demonstrates superior performance compared to existing one-shot methods. In summary, SAT requires only a single pixel-level annotated reference image to perform tissue segmentation across various medical images in a training-free manner. This not only significantly reduces the annotation costs of applying foundational models to the medical field but also enhances task transferability, providing a foundation for the clinical application of intelligent medicine. Our source code is available at <span><span>https://github.com/SnowRain510/Segment-Any-Tissue</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"102 ","pages":"Article 103550"},"PeriodicalIF":10.7,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143675554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TriDeNT : Triple deep network training for privileged knowledge distillation in histopathology","authors":"Lucas Farndale ,&nbsp;Robert Insall ,&nbsp;Ke Yuan","doi":"10.1016/j.media.2025.103479","DOIUrl":"10.1016/j.media.2025.103479","url":null,"abstract":"<div><div>Computational pathology models rarely utilise data that will not be available for inference. This means most models cannot learn from highly informative data such as additional immunohistochemical (IHC) stains and spatial transcriptomics. We present TriDeNT <figure><img></figure>, a novel self-supervised method for utilising privileged data that is not available during inference to improve performance. We demonstrate the efficacy of this method for a range of different paired data including immunohistochemistry, spatial transcriptomics and expert nuclei annotations. In all settings, TriDeNT <figure><img></figure> outperforms other state-of-the-art methods in downstream tasks, with observed improvements of up to 101%. Furthermore, we provide qualitative and quantitative measurements of the features learned by these models and how they differ from baselines. TriDeNT <figure><img></figure> offers a novel method to distil knowledge from scarce or costly data during training, to create significantly better models for routine inputs.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"102 ","pages":"Article 103479"},"PeriodicalIF":10.7,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Subtyping breast lesions via collective intelligence based long-tailed recognition in ultrasound","authors":"Ruobing Huang ,&nbsp;Yinyu Ye ,&nbsp;Ao Chang ,&nbsp;Han Huang ,&nbsp;Zijie Zheng ,&nbsp;Long Tan ,&nbsp;Guoxue Tang ,&nbsp;Man Luo ,&nbsp;Xiuwen Yi ,&nbsp;Pan Liu ,&nbsp;Jiayi Wu ,&nbsp;Baoming Luo ,&nbsp;Dong Ni","doi":"10.1016/j.media.2025.103548","DOIUrl":"10.1016/j.media.2025.103548","url":null,"abstract":"<div><div>Breast lesions display a wide spectrum of histological subtypes. Recognizing these subtypes is vital for optimizing patient care and facilitating tailored treatment strategies compared to a simplistic binary classification of malignancy. However, this task relies on invasive biopsy tests, which carry inherent risks and can lead to over-diagnosis, unnecessary expenses, and pain for patients. To avoid this, we propose to infer lesion subtypes from ultrasound images directly. Meanwhile, the incidence rates of different subtypes exhibit a skewed long-tailed distribution that presents substantial challenges for effective recognition. Inspired by collective intelligence in clinical diagnosis to handle complex or rare cases, we proposed a framework–CoDE–to amalgamate diverse expertise of different backbones to bolster robustness across varying scenarios for automated lesion subtyping. It utilizes dual-level balanced individual supervision to fully exploit prior knowledge while considering class imbalance. It is also equipped with a batch-based online competitive distillation module to stimulate dynamic knowledge exchange. Experimental results demonstrate that the model surpassed the state-of-the-art approaches by more than 7.22% in F1-score facing a challenging breast dataset with an imbalance ratio as high as 47.9:1.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"102 ","pages":"Article 103548"},"PeriodicalIF":10.7,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143675550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}