Learning medical visual representations through vision-language pre-training has reached remarkable progress. Despite the promising performance, it still faces challenges, i.e., local alignment lacks interpretability and clinical relevance, and the insufficient internal and external representation learning of image-report pairs. To address these issues, we propose an Anatomical Structure-Guided (ASG) framework. Specifically, we parse raw reports into triplets <anatomical region, finding, existence>, and fully utilize each element as supervision to enhance representation learning. For anatomical region, we design an automatic anatomical region-sentence alignment paradigm in collaboration with radiologists, considering them as the minimum semantic units to explore fine-grained local alignment. For finding and existence, we regard them as image tags, applying an image-tag recognition decoder to associate image features with their respective tags within each sample and constructing soft labels for contrastive learning to improve the semantic association of different image-report pairs. We evaluate the proposed ASG framework on two downstream tasks, including five public benchmarks. Experimental results demonstrate that our method outperforms the state-of-the-art methods.
AOR: Anatomical Ontology-Guided Reasoning for Medical Large Multimodal Model in Chest X-Ray Interpretation
Fudan University（复旦大学）；Xidian University（西安电子科技大学）；KAIST（韩国科学技术院）；Children’s Hospital of Fudan University（复旦大学附属儿童医院）；Shanghai AI Laboratory（上海人工智能实验室）；Hong Kong Polytechnic University（香港理工大学）
ProSAC-CT: Progressive Spectral-Anatomical Co-Guided Multi-Stage Diffusion Model for Low-Dose CT Denoising
College of Medicine and Biological Information Engineering, Northeastern University（东北大学医学与生物信息工程学院）；Tohoku University School of Medicine（东北大学医学院）；State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center（华南肿瘤学国家重点实验室，中山大学肿瘤防治中心）；AI Lab, Tohoku University Hospital（东北大学医院人工智能实验室）
SAME++: A Self-supervised Anatomical eMbeddings Enhanced medical image registration framework using stable sampling and regularized transformation
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Alibaba Group（阿里巴巴集团）；Johns Hopkins University（约翰斯·霍普金斯大学）；The First Affiliated Hospital of Zhejiang University（浙江大学附属第一医院）
Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysis
Institute for Signals, Systems and Computational Intelligence, sinc(i) CONICET-UNL（信号、系统与计算智能研究所，sinc(i) 阿根廷国家科学技术研究委员会-国立利托拉尔大学）；Hospital Italiano de Buenos Aires（布宜诺斯艾利斯意大利医院）；Universidad Tecnológica Nacional（国立技术大学）
Learning Deformable Registration of Medical Images with Anatomical Constraints
Research institute for signals, systems and computational intelligence, sinc(i)（信号、系统与计算智能研究所（sinc(i)））；FICH-UNL（国立罗萨里奥大学医学与生物化学研究所）；CONICET（阿根廷国家科学与技术研究委员会）
L2GNet: Optimal Local-to-Global Representation of Anatomical Structures for Generalized Medical Image Segmentation
Northwestern University（西北大学）；Beth Israel Lahey Health, University of Massachusetts Chan School of Medicine（马萨诸塞大学陈医学院贝斯以色列拉希医疗中心）；TCS Research（塔塔咨询服务研究中心）；University of South Dakota（南达科他大学）
UAE: Universal Anatomical Embedding on Multi-modality Medical Images
National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University（西北工业大学计算机科学与工程学院，空天地海一体化大数据应用技术国家工程实验室）；Department of Electronic Engineering, The Chinese University of Hong Kong（香港中文大学电子工程系）；Beijing United Family Hospital（北京和睦家医院）；The First Affiliated Hospital, Zhejiang University（浙江大学医学院附属第一医院）；Peking Union Medical College Hospital（北京协和医院）
AD-GS: Anatomical Density-guided 3D Gaussian Splatting for Sparse-View Dental CBCT Reconstruction
Beijing University of Posts and Telecommunications（北京邮电大学）；Peking University Third Hospital（北京大学第三医院）；National Institute of Informatics（信息学研究所）；Peking University（北京大学）
Invisible Yet Detected: PelFANet with Attention-Guided Anatomical Fusion for Pelvic Fracture Diagnosis
Center for Computational & Data Sciences, Independent University, Bangladesh（孟加拉国独立大学计算与数据科学中心）；Department of Computer Science and Engineering, Independent University, Bangladesh（孟加拉国独立大学计算机科学与工程系）；Advanced Medical Engineering Research Institute, University of Hyogo, Japan（日本兵库大学先进医学工程研究所）；Graduate School of Engineering, University of Hyogo, Japan（日本兵库大学工学研究科）
R$^{2}$Seg: Training-Free OOD Medical Tumor Segmentation via Anatomical Reasoning and Statistical Rejection
Carnegie Mellon University（卡内基梅隆大学）；University of Cambridge（剑桥大学）；Zhejiang University（浙江大学）；ETH Zurich（苏黎世联邦理工学院）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
SMART tracking: Simultaneous anatomical imaging and real-time passive device tracking for MR-guided interventions
Image Sciences Institute, University Medical Center Utrecht（乌得勒支大学医学中心影像科学研究所）；University Medical Center Utrecht（乌得勒支大学医学中心）；Utrecht（乌得勒支（城市名，此处不单独作为机构，仅保留原英文机构名中的城市信息但按要求不提取城市））
Detailed Annotations of Chest X-Rays via CT Projection for Report Understanding
Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Daimler TSS（戴姆勒TSS公司）；University Hospital Heidelberg（海德堡大学医院）；German Cancer Research Center（德国癌症研究中心）；University Clinic Essen（埃森大学医院）
Medical Image Registration Meets Vision Foundation Model: Prototype Learning and Contour Awareness
The University of Sydney（悉尼大学）；Harvard Medical School（哈佛医学院）；University of Electronic Science and Technology of China（电子科技大学）；Brigham and Women’s Hospital（布莱根妇女医院）
针对无监督医学图像配准缺乏解剖知识的问题，提出融合 SAM 分割先验、原型学习与轮廓感知损失的配准框架，在多数据集上显著提升复杂结构和模糊边界场景的配准性能。
Modality-Agnostic Structural Image Representation Learning for Deformable Multi-Modality Medical Image Registration
DAMO Academy, Alibaba Group（阿里巴巴达摩院）；Hupan Lab（湖畔实验室）；College of Computer Science and Technology, Zhejiang University, China（浙江大学计算机科学与技术学院）；Shengjing Hospital of China Medical University, China（中国医科大学附属盛京医院）
