Pre-training lays the foundation for recent successes in radiograph analysis supported by deep learning. It learns transferable image representations by conducting large-scale fully-supervised or self-supervised learning on a source domain. However, supervised pre-training requires a complex and labor intensive two-stage human-assisted annotation process while self-supervised learning cannot compete with the supervised paradigm. To tackle these issues, we propose a cross-supervised methodology named REviewing FreE-text Reports for Supervision (REFERS), which acquires free supervision signals from original radiology reports accompanying the radiographs. The proposed approach employs a vision transformer and is designed to learn joint representations from multiple views within every patient study. REFERS outperforms its transfer learning and self-supervised learning counterparts on 4 well-known X-ray datasets under extremely limited supervision. Moreover, REFERS even surpasses methods based on a source domain of radiographs with human-assisted structured labels. Thus REFERS has the potential to replace canonical pre-training methodologies.
Massachusetts Institute of Technology（麻省理工学院）；Philips Research North America（飞利浦北美研究院）；Beth Israel Deaconess Medical Center, Harvard Medical School（哈佛医学院贝斯以色列女执事医疗中心）
We propose and demonstrate a novel machine learning algorithm that assesses pulmonary edema severity from chest radiographs. While large publicly available datasets of chest radiographs and free-text radiology reports exist, only limited numerical edema severity labels can be extracted from radiology reports. This is a significant challenge in learning such models for image classification. To take advantage of the rich information present in the radiology reports, we develop a neural network model that is trained on both images and free-text to assess pulmonary edema severity from chest radiog…
We propose a data collecting and annotation pipeline that extracts information from Vietnamese radiology reports to provide accurate labels for chest X-ray (CXR) images. This can benefit Vietnamese radiologists and clinicians by annotating data that closely match their endemic diagnosis categories which may vary from country to country. To assess the efficacy of the proposed labeling technique, we built a CXR dataset containing 9,752 studies and evaluated our pipeline using a subset of this dataset. With an F1-score of at least 0.9923, the evaluation demonstrates that our labeling tool perform…
Mars Digital Technologies（玛氏数字技术公司）；Antech Imaging Services（安泰奇影像服务公司）；Stanford University（斯坦福大学）
This work describes the development and real-world deployment of a deep learning-based AI system for evaluating canine and feline radiographs across a broad range of findings and abnormalities. We describe a new semi-supervised learning approach that combines NLP-derived labels with self-supervised training leveraging more than 2.5 million x-ray images. Finally we describe the clinical deployment of the model including system architecture, real-time performance evaluation and data drift detection.
Chest radiographs are among the most frequently acquired images in radiology and are often the subject of computer vision research. However, most of the models used to classify chest radiographs are derived from openly available deep neural networks, trained on large image-datasets. These datasets routinely differ from chest radiographs in that they are mostly color images and contain several possible image classes, while radiographs are greyscale images and often only contain fewer image classes. Therefore, very deep neural networks, which can represent more complex relationships in image-fea…
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）；Massachusetts Institute of Technology（麻省理工学院）；Philips Research（飞利浦研究院）
Purpose: To develop a machine learning model to classify the severity grades of pulmonary edema on chest radiographs. Materials and Methods: In this retrospective study, 369,071 chest radiographs and associated radiology reports from 64,581 (mean age, 51.71; 54.51% women) patients from the MIMIC-CXR chest radiograph dataset were included. This dataset was split into patients with and without congestive heart failure (CHF). Pulmonary edema severity labels from the associated radiology reports were extracted from patients with CHF as four different ordinal levels: 0, no edema; 1, vascular conges…
Structure is Supervision: Multiview Masked Autoencoders for Radiology
Department of Computer Science, ETH Zurich（苏黎世联邦理工学院计算机科学系）；Department of Computer Science, UC Irvine（加州大学尔湾分校计算机科学系）；Department of Quantitative Biomedicine, University of Zurich（苏黎世大学定量生物医学系）
Argus: Benchmarking and Enhancing Vision-Language Models for 3D Radiology Report Generation
Imperial College London（帝国理工学院）；The Ohio State University（俄亥俄州立大学）；University of Hong Kong（香港大学）；Hong Kong University of Science and Technology（香港科技大学）
Phu Tho Department of Health（富寿省卫生厅）；Vingroup Big Data Institute（Vingroup大数据研究院）；VinUniversity（VinUniversity大学）；Phu Tho General Hospital（富寿综合医院）
Computer-Aided Diagnosis (CAD) systems for chest radiographs using artificial intelligence (AI) have recently shown a great potential as a second opinion for radiologists. The performances of such systems, however, were mostly evaluated on a fixed dataset in a retrospective manner and, thus, far from the real performances in clinical practice. In this work, we demonstrate a mechanism for validating an AI-based system for detecting abnormalities on X-ray scans, VinDr-CXR, at the Phu Tho General Hospital - a provincial hospital in the North of Vietnam. The AI system was directly integrated into …
HIDSS4Health - Helmholtz Information and Data Science School for Health（HIDSS4Health——亥姆霍兹健康信息与数据科学学院）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；German Cancer Research Center Heidelberg（海德堡德国癌症研究中心）
The lack of fine-grained annotations hinders the deployment of automated diagnosis systems, which require human-interpretable justification for their decision process. In this paper, we address the problem of weakly supervised identification and localization of abnormalities in chest radiographs. To that end, we introduce a novel loss function for training convolutional neural networks increasing the \emph{localization confidence} and assisting the overall \emph{disease identification}. The loss leverages both image- and patch-level predictions to generate auxiliary supervision. Rather than fo…
University of Toronto（多伦多大学）；The Hospital for Sick Children (SickKids)（病童医院（SickKids））；Vector Institute（向量研究所）
As deep learning is widely used in the radiology field, the explainability of such models is increasingly becoming essential to gain clinicians' trust when using the models for diagnosis. In this research, three experiment sets were conducted with a U-Net architecture to improve the classification performance while enhancing the heatmaps corresponding to the model's focus through incorporating heatmap generators during training. All of the experiments used the dataset that contained chest radiographs, associated labels from one of the three conditions ("normal", "congestive heart failure (CHF)…
Automatic radiology report generation has been an attracting research problem towards computer-aided diagnosis to alleviate the workload of doctors in recent years. Deep learning techniques for natural image captioning are successfully adapted to generating radiology reports. However, radiology image reporting is different from the natural image captioning task in two aspects: 1) the accuracy of positive disease keyword mentions is critical in radiology image reporting in comparison to the equivalent importance of every single word in a natural image caption; 2) the evaluation of reporting qua…
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…
Large, labeled datasets have driven deep learning methods to achieve expert-level performance on a variety of medical imaging tasks. We present CheXpert, a large dataset that contains 224,316 chest radiographs of 65,240 patients. We design a labeler to automatically detect the presence of 14 observations in radiology reports, capturing uncertainties inherent in radiograph interpretation. We investigate different approaches to using the uncertainty labels for training convolutional neural networks that output the probability of these observations given the available frontal and lateral radiogra…
Institute of Cancer Research（癌症研究所）；Universidad de Valladolid（巴利亚多利德大学）；AGH University of Science and Technology（AGH科技大学）；Cardiff University（卡迪夫大学）；Aberystwyth University（阿伯里斯特威斯大学）
Gliomas are brain tumours with a high mortality rate. There are various grades and sub-types of this tumour, and the treatment procedure varies accordingly. Clinicians and oncologists diagnose and categorise these tumours based on visual inspection of radiology and histology data. However, this process can be time-consuming and subjective. The computer-assisted methods can help clinicians to make better and faster decisions. In this paper, we propose a pipeline for automatic classification of gliomas into three sub-types: oligodendroglioma, astrocytoma, and glioblastoma, using both radiology a…
Opportunities and challenges in the application of large artificial intelligence models in radiology
College of Computer Science and Electronic Engineering, Hunan University（湖南大学计算机与通信学院）；The second xiangya hospital, National University of Defense Technology（国防科技大学湘雅二医院）；College of Military and Political Basic Education, National University of Defense Technology（国防科技大学军政基础教育学院）；Fullink Technology Group（福林科技集团）；Institute of Forest Resource Information Techniques, Chinese Academy of Forestry（中国林业科学研究院森林资源信息技术研究所）；The second xiangya hospital, Central South University（中南大学湘雅二医院）
Xiamen University（厦门大学）；The University of Hong Kong（香港大学）
Modern studies in radiograph representation learning rely on either self-supervision to encode invariant semantics or associated radiology reports to incorporate medical expertise, while the complementarity between them is barely noticed. To explore this, we formulate the self- and report-completion as two complementary objectives and present a unified framework based on masked record modeling (MRM). In practice, MRM reconstructs masked image patches and masked report tokens following a multi-task scheme to learn knowledge-enhanced semantic representations. With MRM pre-training, we obtain pre…
When Repository Labels Are Not Image-Level Truth: A Supervision Auditing Framework for Chest Radiograph AI
Medical Imaging and AI in Healthcare Research Group, Biosciences Center, SURA（SURA生物科学中心医疗成像与医疗保健人工智能研究组）；Universidad de Antioquia（安蒂奥基亚大学）；IBM Research（IBM研究院）；Neuroradiology Institute, Universitätsklinikum Erlangen（埃尔兰根大学医院神经放射学研究所）
Radiology reports are an important means of communication between radiologists and other physicians. These reports express a radiologist's interpretation of a medical imaging examination and are critical in establishing a diagnosis and formulating a treatment plan. In this paper, we propose a Bi-directional convolutional neural network (Bi-CNN) model for the interpretation and classification of mammograms based on breast density and chest radiographic radiology reports based on the basis of chest pathology. The proposed approach helps to organize databases of radiology reports, retrieve them e…
GreenRFM: Learning a resource-efficient radiology vision-language foundation model via supervision-centric pre-training
School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China (USTC)（生物医学工程学院，生命科学与医学系，中国科学技术大学）；Center for Medical Imaging, Robotics, Analytic Computing & Learning (MIRACLE)（医学影像、机器人、分析计算与学习中心）；Suzhou Institute for Advanced Research, USTC（苏州先进研究院，中国科学技术大学）；Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, USTC（放射科，中国科学技术大学第一附属医院，生命科学与医学系，中国科学技术大学）；T Magnetic Resonance Translational Medicine Research Center, Department of Radiology, The First Affiliated Hospital (Southwest Hospital) of Army Medical University（7T磁共振转化医学研究中心，放射科，中国医学大学第一附属医院（西南医院））
AlphaRAD: Grounded Zero-Shot Classification in Chest Radiology via $α$-Corrected Binary Cross Entropy and Factorized Latent Supervision
Peter Munk Cardiac Centre, University Health Network (UHN)（彼得·芒克心脏中心，大学健康网络（UHN））；Ted Rogers Centre for Heart Research（泰德·罗杰斯心脏研究中心）；University of Toronto (U of T)（多伦多大学）；Vector Institute（向量研究所）；Toronto General Hospital Research Institute, UHN（多伦多总医院研究所，大学健康网络（UHN））
The University of Edinburgh（爱丁堡大学）；Canon Medical Research Europe（佳能医疗欧洲研究中心）；The Alan Turing Institute（艾伦·图灵研究所）
When a clinician refers a patient for an imaging exam, they include the reason (e.g. relevant patient history, suspected disease) in the scan request; this appears as the indication field in the radiology report. The interpretation and reporting of the image are substantially influenced by this request text, steering the radiologist to focus on particular aspects of the image. We use the indication field to drive better image classification, by taking a transformer network which is unimodally pre-trained on text (BERT) and fine-tuning it for multimodal classification of a dual image-text input…
Yale University（耶鲁大学）；Rice University（莱斯大学）；University of Texas Health Science Center（德克萨斯大学健康科学中心）
The proliferation of Deep Learning (DL)-based methods for radiographic image analysis has created a great demand for expert-labeled radiology data. Recent self-supervised frameworks have alleviated the need for expert labeling by obtaining supervision from associated radiology reports. These frameworks, however, struggle to distinguish the subtle differences between different pathologies in medical images. Additionally, many of them do not provide interpretation between image regions and text, making it difficult for radiologists to assess model predictions. In this work, we propose Local Regi…
University at Buffalo, The State University of New York（纽约州立大学布法罗分校）；Weill Cornell Medicine（威尔康奈尔医学院）
Self-supervised learning provides an opportunity to explore unlabeled chest X-rays and their associated free-text reports accumulated in clinical routine without manual supervision. This paper proposes a Joint Image Text Representation Learning Network (JoImTeRNet) for pre-training on chest X-ray images and their radiology reports. The model was pre-trained on both the global image-sentence level and the local image region-word level for visual-textual matching. Both are bidirectionally constrained on Cross-Entropy based and ranking-based Triplet Matching Losses. The region-word matching is ca…
RadPRISM: Schema-stratified radiology-report supervision for concept-disentangled image representations and visual grounding
Technical University of Munich (TUM)（慕尼黑工业大学（TUM））；TUM University Hospital（慕尼黑工业大学医院）；Technical University of Munich, School of Medicine and Health（慕尼黑工业大学医学与健康学院）；Klinikum rechts der Isar（右伊萨尔医院）；Charité – Universitätsmedizin Berlin（柏林夏里特医学院）；Freie Universität Berlin（柏林自由大学）；Humboldt Universität zu Berlin（柏林洪堡大学）；Imperial College London（伦敦帝国理工学院）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心（MCML））；University Hospital Essen (AöR)（埃森大学医院（AöR））；Institute for Artificial Intelligence in Medicine (IKIM)（医学人工智能研究所（IKIM））；Institute of Interventional and Diagnostic Radiology and Neuroradiology（介入与诊断放射学及神经放射学研究所）；National Center for Tumor Diseases West（西部肿瘤疾病国家中心）
CheXanatomy: Anatomy-Aware Vision-Language Modeling for Chest Radiographs
Stanford Center for Artificial Intelligence in Medicine and Imaging, Stanford University（斯坦福大学医学与影像人工智能中心）；Department of Radiology, Stanford University（斯坦福大学放射学系）
University of Rochester（罗切斯特大学）；Ludwig Maximilian University（路德维希马克西米利安大学）
Chest x-rays are the most common radiology studies for diagnosing lung and heart disease. Hence, a system for automated pre-reporting of pathologic findings on chest x-rays would greatly enhance radiologists' productivity. To this end, we investigate a deep-learning framework with novel training schemes for classification of different thoracic pathology labels from chest x-rays. We use the currently largest publicly available annotated dataset ChestX-ray14 of 112,120 chest radiographs of 30,805 patients. Each image was annotated with either a 'NoFinding' class, or one or more of 14 thoracic pa…
University of Rochester（罗切斯特大学）；Futurewei Technologies, Inc.（未来 Wei 技术公司）
Generating radiology reports is time-consuming and requires extensive expertise in practice. Therefore, reliable automatic radiology report generation is highly desired to alleviate the workload. Although deep learning techniques have been successfully applied to image classification and image captioning tasks, radiology report generation remains challenging in regards to understanding and linking complicated medical visual contents with accurate natural language descriptions. In addition, the data scales of open-access datasets that contain paired medical images and reports remain very limite…
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）
John Hopkins University（约翰霍普金斯大学）；University of Bologna（博洛尼亚大学）；Italian Institute of Technology（意大利技术研究院）；University of California, San Francisco（旧金山大学）；University of California, Berkeley（伯克利大学）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）
National University of Singapore（新加坡国立大学）；Institute for Infocomm Research, A*STAR（新加坡科技研究局资讯通信研究院）；Singapore University of Technology and Design（新加坡科技设计大学）
Deep learning approaches achieve state-of-the-art performance for classifying radiology images, but rely on large labelled datasets that require resource-intensive annotation by specialists. Both semi-supervised learning and active learning can be utilised to mitigate this annotation burden. However, there is limited work on combining the advantages of semi-supervised and active learning approaches for multi-label medical image classification. Here, we introduce a novel Consistency-based Semi-supervised Evidential Active Learning framework (CSEAL). Specifically, we leverage predictive uncertai…
A generalizable large-scale foundation model for musculoskeletal radiographs
Seoul National University Hospital（首尔大学医院）；Seoul National University（首尔大学）；Seoul National University College of Medicine（首尔大学医学院）；Seoul National University Bundang Hospital（首尔大学盆唐医院）
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）
