Recent foundation models have demonstrated strong performance in medical image representation learning, yet their comparative behaviour across datasets remains underexplored. This work benchmarks two large-scale chest X-ray (CXR) embedding models (CXR-Foundation (ELIXR v2.0) and MedImagelnsight) on public MIMIC-CR and NIH ChestX-ray14 datasets. Each model was evaluated using a unified preprocessing pipeline and fixed downstream classifiers to ensure reproducible comparison. We extracted embeddings directly from pre-trained encoders, trained lightweight LightGBM classifiers on multiple disease labels, and reported mean AUROC, and F1-score with 95% confidence intervals. MedImageInsight achieved slightly higher performance across most tasks, while CXR-Foundation exhibited strong cross-dataset stability. Unsupervised clustering of MedImageIn-sight embeddings further revealed a coherent disease-specific structure consistent with quantitative results. The results highlight the need for standardised evaluation of medical foundation models and establish reproducible baselines for future multimodal and clinical integration studies.
Automatic Chest Radiograph X-ray (CXR) interpretation by machines is an important research topic of Artificial Intelligence. As part of my journey through the California Science Fair, I have developed an algorithm that can detect pneumonia from a CXR image to compensate for human fallibility. My algorithm, PneumoXttention, is an ensemble of two 13 layer convolutional neural network trained on the RSNA dataset, a dataset provided by the Radiological Society of North America, containing 26,684 frontal X-ray images split into the categories of pneumonia and no pneumonia. The dataset was annotated…
Toward Clinically Acceptable Chest X-ray Report Generation: A Qualitative Retrospective Pilot Study of CXRMate-2
organization= Australian e-Health Research Centre, CSIRO Health；organization= Princess Alexandra Hospital, Metro South Health , city= Brisbane , country= Australia；organization= Queensland Digital Health Centre, University of Queensland , city= Brisbane , country= Australia；Informatics Directorate, Metro South Hospital；organization= School of Electrical Engineering；Computer Science, University of Queensland , city= Brisbane , country= Australia
National Tsing Hua University（国立清华大学）；Massachusetts Institute of Technology（麻省理工学院）；Emory University（埃默里大学）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Emory University School of Medicine（埃默里大学医学院）
Chronic obstructive pulmonary disease (COPD) is one of the most common chronic illnesses in the world and the third leading cause of mortality worldwide. It is often underdiagnosed or not diagnosed until later in the disease course. Spirometry tests are the gold standard for diagnosing COPD but can be difficult to obtain, especially in resource-poor countries. Chest X-rays (CXRs), however, are readily available and may serve as a screening tool to identify patients with COPD who should undergo further testing. Currently, no research applies deep learning (DL) algorithms that use large multi-si…
Institute of Medical Engineering & Science, Massachusetts Institute of Technology（麻省理工学院医学工程与科学研究所）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Stanford University（斯坦福大学）；Harvard Medical School（哈佛医学院）；National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health（美国国立卫生研究院国家医学图书馆国家生物技术信息中心）
Chest radiography is an extremely powerful imaging modality, allowing for a detailed inspection of a patient's thorax, but requiring specialized training for proper interpretation. With the advent of high performance general purpose computer vision algorithms, the accurate automated analysis of chest radiographs is becoming increasingly of interest to researchers. However, a key challenge in the development of these techniques is the lack of sufficient data. Here we describe MIMIC-CXR-JPG v2.0.0, a large dataset of 377,110 chest x-rays associated with 227,827 imaging studies sourced from the B…
Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology（中国科学院深圳先进技术研究院保罗·C·劳特伯生物医学成像研究中心）；University of Chinese Academy of Sciences（中国科学院大学）；Pengcheng Laboratory（鹏城实验室）；Renmin Hospital of Wuhan University（武汉大学人民医院）
Multi-modal representation methods have achieved advanced performance in medical applications by extracting more robust features from multi-domain data. However, existing methods usually need to train additional branches for downstream tasks, which may increase the model complexities in clinical applications as well as introduce additional human inductive bias. Besides, very few studies exploit the rich clinical knowledge embedded in clinical daily reports. To this end, we propose a novel medical generalist agent, MGA, that can address three kinds of common clinical tasks via clinical reports …
HIST-AID: Leveraging Historical Patient Reports for Enhanced Multi-Modal Automatic Diagnosis
Courant Institute of Mathematical Sciences, New York University（纽约大学柯朗数学科学研究所）；New York University Grossman School of Medicine（纽约大学格罗斯曼医学院）；Center of Data Science, New York University（纽约大学数据科学中心）；Prescient Design, Genentech（基因泰克公司Prescient Design部门）；CIFAR LMB（加拿大高级研究院学习算法与机器智能实验室）
Enhanced Contrastive Learning with Multi-view Longitudinal Data for Chest X-ray Report Generation
School of Computer Science and Technology, Xidian University（西安电子科技大学计算机科学与技术学院）；Xi’an Key Laboratory of Big Data and Intelligent Vision（西安大数据与智能视觉重点实验室）；Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, Xidian University（教育部协同智能系统重点实验室）；Warren Alpert Medical School, Brown University（布朗大学沃伦·阿尔佩特医学学院）
Frozen Foundation-Model Embeddings Discard Small-Lesion Signal in Chest Radiography: Implications for Pre-Deployment Evaluation
Department of Biomedical Engineering and Informatics, Indiana University（印第安纳大学生物医学工程与信息学系）；Department of Radiology and Imaging Sciences, Emory University（埃默里大学放射学与影像科学系）
Harvard University（哈佛大学）；Institute for Applied Computational Science（应用计算科学研究所）
Heart failure remains a major public health challenge with growing costs. Ejection fraction (EF) is a key metric for the diagnosis and management of heart failure however estimation of EF using echocardiography remains expensive for the healthcare system and subject to intra/inter operator variability. While chest x-rays (CXR) are quick, inexpensive, and require less expertise, they do not provide sufficient information to the human eye to estimate EF. This work explores the efficacy of computer vision techniques to predict reduced EF solely from CXRs. We studied a dataset of 3488 CXRs from th…
FG-CXR: A Radiologist-Aligned Gaze Dataset for Enhancing Interpretability in Chest X-Ray Report Generation
University of Arkansas（阿肯色大学）；West Virginia University（西弗吉尼亚大学）；University of Liverpool（利物浦大学）；MD Anderson Cancer Center（MD安德森癌症中心）；University of Houston（休斯顿大学）
Acute Care Solutions（急性护理解决方案）；Philips Research North America（飞利浦北美研究院）
The MIMIC-CXR dataset is (to date) the largest released chest x-ray dataset consisting of 473,064 chest x-rays and 206,574 radiology reports collected from 63,478 patients. We present the results of training and evaluating a collection of deep convolutional neural networks on this dataset to recognize multiple common thorax diseases. To the best of our knowledge, this is the first work that trains CNNs for this task on such a large collection of chest x-ray images, which is over four times the size of the largest previously released chest x-ray corpus (ChestX-Ray14). We describe and evaluate i…
Leveraging the Structure of Medical Data for Improved Representation Learning
Department of Computer Science, ETH Zurich, Zurich, Switzerland（苏黎世联邦理工学院计算机科学系）；Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland（苏黎世大学定量生物医学系）
Emory School of Medicine（埃默里医学院）；MentorGraphics Indian Pvt. Ltd.（MentorGraphics印度私人有限公司）；K. N. Toosi University of Technology（K.N.托西理工大学）；EMORY Hospital（埃默里医院）
Purpose: Since the recent COVID-19 outbreak, there has been an avalanche of research papers applying deep learning based image processing to chest radiographs for detection of the disease. To test the performance of the two top models for CXR COVID-19 diagnosis on external datasets to assess model generalizability. Methods: In this paper, we present our argument regarding the efficiency and applicability of existing deep learning models for COVID-19 diagnosis. We provide results from two popular models - COVID-Net and CoroNet evaluated on three publicly available datasets and an additional ins…
Feinberg School of Medicine（范伯格医学院）；Northwestern University（西北大学）
Joint image-text embedding extracted from medical images and associated contextual reports is the bedrock for most biomedical vision-and-language (V+L) tasks, including medical visual question answering, clinical image-text retrieval, clinical report auto-generation. In this study, we adopt four pre-trained V+L models: LXMERT, VisualBERT, UNIER and PixelBERT to learn multimodal representation from MIMIC-CXR radiographs and associated reports. The extrinsic evaluation on OpenI dataset shows that in comparison to the pioneering CNN-RNN model, the joint embedding learned by pre-trained V+L models…
CheXmask: a large-scale dataset of anatomical segmentation masks for multi-center chest x-ray images
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) CONICET-UNL））；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）
Enhancing Network Initialization for Medical AI Models Using Large-Scale, Unlabeled Natural Images
University Hospital RWTH Aachen（亚琛工业大学附属医院）；Technische Universität Dresden（德累斯顿工业大学）；University Hospital Carl Gustav Carus Dresden（德累斯顿卡尔·古斯塔夫·卡鲁斯大学医院）；Else Kröner Fresenius Center for Digital Health（Else Kröner Fresenius 数字健康中心）；University Hospital Heidelberg（海德堡大学医院）；National Center for Tumor Diseases（国家肿瘤疾病中心）
