Vision-language foundation models have emerged as powerful general-purpose representation learners with strong potential for multimodal understanding, but their deterministic embeddings often fail to provide the reliability required for high-stakes biomedical applications. This work introduces MedProbCLIP, a probabilistic vision-language learning framework for chest X-ray and radiology report representation learning and bidirectional retrieval. MedProbCLIP models image and text representations as Gaussian embeddings through a probabilistic contrastive objective that explicitly captures uncertainty and many-to-many correspondences between radiographs and clinical narratives. A variational information bottleneck mitigates overconfident predictions, while MedProbCLIP employs multi-view radiograph encoding and multi-section report encoding during training to provide fine-grained supervision for clinically aligned correspondence, yet requires only a single radiograph and a single report at inference. Evaluated on the MIMIC-CXR dataset, MedProbCLIP outperforms deterministic and probabilistic baselines, including CLIP, CXR-CLIP, and PCME++, in both retrieval and zero-shot classification. Beyond accuracy, MedProbCLIP demonstrates superior calibration, risk-coverage behavior, selective retrieval reliability, and robustness to clinically relevant corruptions, underscoring the value of probabilistic vision-language modeling for improving the trustworthiness and safety of radiology image-text retrieval systems.
Resolution scaling governs DINOv3 transfer performance in chest radiograph classification
Lab for AI in Medicine, RWTH Aachen University（人工智能医学实验室，亚琛工业大学）；Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen（诊断与介入放射学部门，亚琛大学医院）；Department of Urology, Stanford University（泌尿科，斯坦福大学）；Department of Radiology, Stanford University（放射科，斯坦福大学）；Institute for Computational Genomics, Joint Research Center for Computational Biomedicine, University Hospital RWTH Aachen（计算基因组研究所，计算生物医学联合研究中心，亚琛大学医院）；Else Kroener Fresenius Center for Digital Health, Technical University Dresden（数字健康中心，德累斯顿技术大学）；Department of Medicine I, University Hospital Dresden（第一医学部，德累斯顿大学医院）；National Center for Tumor Diseases (NCT), University Hospital Heidelberg（肿瘤疾病国家中心（NCT），海德堡大学医院）
Google-MedGemma Based Abnormality Detection in Musculoskeletal radiographs
University of Texas at Arlington, Texas, United States（德克萨斯大学阿灵顿分校）；University of Engineering and Management, Kolkata, India（工程与管理大学）；Sardar Vallabhbhai National Institute of Technology, Surat, India（萨达尔·瓦拉布尔希国家理工学院）
ViT3Flow: A Test-Time Training Transformer MeanFlow for Postoperative Radiograph Synthesis in Scoliosis
The University of Hong Kong（香港大学）；The University of Hong Kong-Shenzhen Hospital（香港大学深圳医院）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；The Chinese University of Hong Kong（香港中文大学）；Qilu Hospital of Shandong University（山东大学齐鲁医院）
RadTex: Learning Efficient Radiograph Representations from Text Reports
MIT Lincoln Laboratory（麻省理工学院林肯实验室）；Massachusetts Institute of Technology（麻省理工学院）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Harvard Medical School（哈佛医学院）
From Classification to Localization and Clinical Validation: Large-Scale Development of a Deep Learning System for Thoracic Disease Detection on Chest Radiographs in Thailand
Perceptra Co., Ltd.（Perceptra有限公司）；Radiology Department, Faculty of Medicine Siriraj Hospital, Mahidol University（玛希隆大学诗里拉吉医院医学院放射科）；Institute of Field Robotics, King Mongkut’s University of Technology Thonburi（泰国国王科技大学吞武里分校野外机器人研究所）
X2CT-CLIP: Enable Multi-Abnormality Detection in Computed Tomography from Chest Radiography via Tri-Modal Contrastive Learning
Peter Munk Cardiac Centre（彼得·芒克心脏中心）；University Health Network (UHN)（大学健康网络）；University of Toronto (U of T)（多伦多大学）；Ted Rogers Centre for Heart Research（泰德·罗杰斯心脏研究中心）；Toronto General Hospital Research Institute（多伦多综合医院研究所）；Vector Institute（矢量研究所）
Federated LoRA Adaptation of BiomedCLIP Across Four International Chest X-Ray Cohorts
North Carolina A&T State University（北卡罗来纳农工州立大学）；Tribhuvan University（特里布文大学）；University of Tennessee-Knoxville（田纳西大学诺克斯维尔分校）；University of South Dakota（南达科他大学）
AMAE: Adaptation of Pre-Trained Masked Autoencoder for Dual-Distribution Anomaly Detection in Chest X-Rays
École Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院（EPFL））；Lausanne University Hospital (CHUV)（洛桑大学医院（CHUV））；Inception Institute of AI (IIAI)（Inception人工智能研究院（IIAI））
Exploring the Capabilities of Large Language Model Encoders for Image-Text Retrieval in Chest X-rays
Interdisciplinary Program in Bioengineering, Seoul National University Graduate School（生物工程跨学科项目，首尔国立大学研究生院）；Integrated Major in Innovative Medical Science, Seoul National University Graduate School（创新医学科学整合专业，首尔国立大学研究生院）；Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine（浙江大学医学院第一附属医院放射科）；Seoul National University College of Medicine（首尔国立大学医学院）；Department of Radiology, Seoul National University College of Medicine, Seoul National University Hospital（首尔国立大学医学院放射科，首尔国立大学医院）；Institute of Medical and Biological Engineering, Seoul National University Medical Research Center（医学与生物工程研究所，首尔国立大学医学研究所以及）；Institute of Radiation Medicine, Seoul National University Medical Research Center（放射医学研究所，首尔国立大学医学研究所以及）
RadJEPA: Radiology Encoder for Chest X-Rays via Joint Embedding Predictive Architecture
Department of Computer Science and Engineering, Indian Institute of Technology Bombay（印度理工学院孟买分校计算机科学与工程系）；Department of Biomedical Engineering, Johns Hopkins University（约翰霍普金斯大学生物医学工程系）；Koita Centre for Digital Health, Indian Institute of Technology Bombay（印度理工学院孟买分校Koita数字健康中心）
LLaMA-XR: A Novel Framework for Radiology Report Generation using LLaMA and QLoRA Fine Tuning
Department of Computer Science and Engineering, Southeast University（计算机科学与工程系，东南大学）；School of Computing, Mathematics and Engineering, Charles Sturt University（计算、数学与工程学院，查尔斯·斯特劳特大学）；Department of Computer Science and Engineering, University of Liberal Arts Bangladesh（计算机科学与工程系，孟加拉国自由大学）；Medical Imaging Group, School of Dentistry and Medical Sciences, Charles Sturt University（医学影像组，牙科学院与医学科学学院，查尔斯·斯特劳特大学）
From Compound Figures to Medical Multi-image Reasoning: Scaling Multimodal Large Language Models with Biomedical Literature
Department of Biomedical Informatics and Data Science, Yale School of Medicine, Yale University, New Haven, CT 06510, USA（耶鲁医学院生物医学信息学与数据科学系，耶鲁大学，新 Haven，CT 06510，USA）；School of Medicine, University of Puerto Rico, San Juan, PR 00921, USA（波多黎各大学医学院，波多黎各，San Juan，PR 00921，USA）
Efficient Medical Vision-Language Alignment Through Adapting Masked Vision Models
School of Informatics, Xiamen University（厦门大学信息学院）；Center for Smart Health, School of Nursing, The Hong Kong Polytechnic University（香港理工大学护理学院智能健康中心）；Department of Biomedical Informatics, Harvard Medical School（哈佛医学院生物医学信息学系）；Department of Radiology, Zhongshan Hospital (Xiamen), Fudan University（复旦大学中山医院放射科）；National Institute for Data Science in Health and Medicine（医学与数据科学国家研究院）；Department of Computer Science, School of Informatics, Xiamen University（厦门大学信息学院计算机科学系）
