Modern deep learning algorithms geared towards clinical adaption rely on a significant amount of high fidelity labeled data. Low-resource settings pose challenges like acquiring high fidelity data and becomes the bottleneck for developing artificial intelligence applications. Ultrasound images, stored in Digital Imaging and Communication in Medicine (DICOM) format, have additional metadata data corresponding to ultrasound image parameters and medical exams. In this work, we leverage DICOM metadata from ultrasound images to help learn representations of the ultrasound image. We demonstrate that the proposed method outperforms the non-metadata based approaches across different downstream tasks.
Automatic classification of prostate MR series type using image content and metadata
Brigham and Women’s Hospital（布莱根妇女医院）；German Cancer Research Center（德国癌症研究中心）；Isomics（伊索米克斯公司）；PixelMed Publishing（PixelMed出版公司）；Massachusetts General Hospital（麻省总医院）
Acquisition state behaves as a structured, measurable variable governing lung-nodule AI: kernel-driven measurement instability and noise-driven detection fragility, invisible to DICOM metadata
Emory University（埃默里大学）；Indiana University Purdue University（印第安纳大学与普渡大学）；Mentor Graphics India Pvt. Ltd.（明导国际印度私人有限公司）；CISSS Lanaudiere（拉纳迪耶综合健康与社会服务中心）；Laval University（拉瓦尔大学）
Executing machine learning (ML) pipelines in real-time on radiology images is hard due to the limited computing resources in clinical environments and the lack of efficient data transfer capabilities to run them on research clusters. We propose Niffler, an integrated framework that enables the execution of ML pipelines at research clusters by efficiently querying and retrieving radiology images from the Picture Archiving and Communication Systems (PACS) of the hospitals. Niffler uses the Digital Imaging and Communications in Medicine (DICOM) protocol to fetch and store imaging data and provide…
MR-CLIP: Efficient Metadata-Guided Learning of MRI Contrast Representations
School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK（生物医学工程与成像科学学院，伦敦国王学院，伦敦，英国）；deepc GMBH, Munich, Germany（deepc 德国慕尼黑分公司）
Medical Image De-Identification Resources: Synthetic DICOM Data and Tools for Validation
University of Arkansas for Medical Sciences（亚拉巴马医学科学大学）；Frederick National Laboratory for Cancer Research（癌症研究国家实验室）；Ellumen, Inc.（Ellumen公司）；Deloitte Consulting LLP（德勤咨询有限公司）；National Cancer Institute（国家癌症研究所）；Pixelmed Publishing（Pixelmed出版公司）；National Heart, Lung, and Blood Institute（国家心脏、肺部和血液研究所）
Microsoft Health and Life Sciences（微软健康与生命科学部）；Stanford Center for Artificial Intelligence in Medicine and Imaging（斯坦福医学与成像人工智能中心）
Clinical Artificial lntelligence (AI) applications are rapidly expanding worldwide, and have the potential to impact to all areas of medical practice. Medical imaging applications constitute a vast majority of approved clinical AI applications. Though healthcare systems are eager to adopt AI solutions a fundamental question remains: \textit{what happens after the AI model goes into production?} We use the CheXpert and PadChest public datasets to build and test a medical imaging AI drift monitoring workflow to track data and model drift without contemporaneous ground truth. We simulate drift in…
Medical Image De-Identification Benchmark Challenge
Frederick National Laboratory for Cancer Research（弗雷德里克国家癌症研究中心）；National Cancer Institute（国家癌症研究所）；University of Arkansas for Medical Sciences（亚利桑那医学科学大学）；PixelMed Publishing（PixelMed出版公司）；Essential Software Inc.（Essential软件公司）；Amazon Web Services (AWS)（亚马逊网络服务（AWS））；University of California（加州大学）；Cedars-Sinai Medical Center（Cedars-Sinai医疗中心）；Stanford University（斯坦福大学）；Biomedical Engineering & Imaging Institute（生物医学工程与成像研究所）；Icahn School of Medicine at Mount Sinai（辛格纳医学中心学院）；University Medical Center Freiburg（弗赖堡大学医学中心）；German Cancer Research Center（德国癌症研究中心）；Heidelberg University Hospital（海德堡大学医院）；GE Healthcare（通用电气医疗）；ShanghaiTech University（上海科技大学）；Vanderbilt University Medical Center（范德比尔大学医学中心）
Unidad Mixta de Imagen Biomédica FISABIO-CIPF（FISABIO-CIPF生物医学成像联合单元）；Universidad de Alicante（阿利坎特大学）；Medbravo；Hospital San Juan de Alicante（阿利坎特圣胡安医院）；Universidad Miguel Hernández（米格尔·埃尔南德斯大学）；Sierra Research SL（Sierra Research有限公司）；Bioinformatics & Biostatistics Unit, Principe Felipe Research Center（费利佩王子研究中心生物信息学与生物统计学单元）
This paper describes BIMCV COVID-19+, a large dataset from the Valencian Region Medical ImageBank (BIMCV) containing chest X-ray images CXR (CR, DX) and computed tomography (CT) imaging of COVID-19+ patients along with their radiological findings and locations, pathologies, radiological reports (in Spanish), DICOM metadata, Polymerase chain reaction (PCR), Immunoglobulin G (IgG) and Immunoglobulin M (IgM) diagnostic antibody tests. The findings have been mapped onto standard Unified Medical Language System (UMLS) terminology and cover a wide spectrum of thoracic entities, unlike the considerab…
Compositional Segmentation of Cardiac Images Leveraging Metadata
School of Electronic Engineering and Computer Science, Queen Mary University of London（伦敦玛丽女王大学电子工程与计算机科学学院）；Queen Mary’s Digital Environment Research Institute (DERI)（玛丽女王数字环境研究所）；Department of Computer Science, University College London（伦敦大学学院计算机科学系）；School of Engineering and Materials Science, Queen Mary University of London（伦敦玛丽女王大学工程与材料科学学院）
Efficient Large Scale Medical Image Dataset Preparation for Machine Learning Applications
German Cancer Research Center (DKFZ)（德国癌症研究中心（DKFZ））；University of Heidelberg（海德堡大学）；Goethe University Frankfurt（法兰克福歌德大学）；Charité - Universitätsmedizin Berlin（柏林夏里特医学院）
UltraEar: a multicentric, large-scale database combining ultra-high-resolution computed tomography and clinical data for ear diseases
Beijing Friendship Hospital, Capital Medical University（首都医科大学附属北京友谊医院）；Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology（北京工业大学计算智能与智能系统北京市重点实验室）；Yanjing Medical College, Capital Medical University（首都医科大学燕京医学院）
本文介绍多中心UltraEar数据库的构建，整合11家医院0.1 mm U-HRCT影像、报告与临床数据，并提供标准化标注、分割和隐私保护流程，以支持耳科AI研究、教学与协作。
Building RadiologyNET: Unsupervised annotation of a large-scale multimodal medical database
University of Rijeka – Faculty of Engineering（里耶卡大学工程学院）；Center for Artificial Intelligence and Cybersecurity（人工智能与网络安全中心）；Medical University of Graz（格拉茨医科大学）
MHub.ai: A Simple, Standardized, and Reproducible Platform for AI Models in Medical Imaging
Mass General Brigham, Harvard Medical School（麻省总布莱根医院、哈佛医学院）；Maastricht University（马斯特里赫特大学）；Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School（丹娜-法伯癌症研究所、布莱根妇女医院、哈佛医学院）；KnowledgeVis, LLC（KnowledgeVis有限责任公司）；Isomics, Inc.（Isomics公司）；Brigham and Women’s Hospital, Harvard Medical School（布莱根妇女医院、哈佛医学院）；Radboud University Medical Center（拉德堡德大学医学中心）；BAMF Health LLC（BAMF Health有限责任公司）；Charité - Universitätsmedizin Berlin（柏林夏里特医学院）；Massachusetts General Hospital and Harvard Medical School（麻省总医院和哈佛医学院）；Memorial Sloan Kettering Cancer Center（纪念斯隆·凯特琳癌症中心）；Technical University of Munich（慕尼黑工业大学）；German Heart Center（德国心脏中心）
MRpro: open framework for model-based, learned, and quantitative MR imaging
Department of Radiology（放射科）；Charité -- Universitätsmedizin Berlin（柏林夏里特大学医学院）；Max Planck Research Group MR Physics（人类发展马克斯·普朗克研究所磁共振物理研究组）；Max Planck Institute for Human Development（人类发展马克斯·普朗克研究所）
