Chest radiography is a general method for diagnosing a patient's condition and identifying important information; therefore, radiography is used extensively in routine medical practice in various situations, such as emergency medical care and medical checkup. However, a high level of expertise is required to interpret chest radiographs. Thus, medical specialists spend considerable time in diagnosing such huge numbers of radiographs. In order to solve these problems, methods for generating findings have been proposed. However, the study of generating chest radiograph findings has primarily focused on the English language, and to the best of our knowledge, no studies have studied Japanese data on this subject. There are two challenges involved in generating findings in the Japanese language. The first challenge is that word splitting is difficult because the boundaries of Japanese word are not clear. The second challenge is that there are numerous orthographic variants. For deal with these two challenges, we proposed an end-to-end model that generates Japanese findings at the character-level from chest radiographs. In addition, we introduced the attention mechanism to improve not only the accuracy, but also the interpretation ability of the results. We evaluated the proposed method using a public dataset with Japanese findings. The effectiveness of the proposed method was confirmed using the Bilingual Evaluation Understudy score. And, we were confirmed from the generated findings that the proposed method was able to consider the orthographic variants. Furthermore, we confirmed via visual inspection that the attention mechanism captures the features and positional information of radiographs.
Pneumothorax is a relatively common disease, but in some cases, it may be difficult to find with chest radiography. In this paper, we propose a novel method of detecting pneumothorax in chest radiography. We propose an ensemble model of identical convolutional neural networks (CNN) with three different sizes of radiography images. Conventional methods may not properly characterize lost features while resizing large size images into 256 x 256 or 224 x 224 sizes. Our model is evaluated with ChestX-ray dataset which contains over 100,000 chest radiography images. As a result of the experiment, th…
Rutgers University（罗格斯大学）；Thomas Jefferson University（托马斯杰斐逊大学）
The purpose of this study is to develop an automated algorithm for thoracic vertebral segmentation on chest radiography using deep learning. 124 de-identified lateral chest radiographs on unique patients were obtained. Segmentations of visible vertebrae were manually performed by a medical student and verified by a board-certified radiologist. 74 images were used for training, 10 for validation, and 40 were held out for testing. A U-Net deep convolutional neural network was employed for segmentation, using the sum of dice coefficient and binary cross-entropy as the loss function. On the test s…
University of Waterloo（滑铁卢大学）；National Research Council Canada（加拿大国家研究委员会）
Building AI models with trustworthiness is important especially in regulated areas such as healthcare. In tackling COVID-19, previous work uses convolutional neural networks as the backbone architecture, which has shown to be prone to over-caution and overconfidence in making decisions, rendering them less trustworthy -- a crucial flaw in the context of medical imaging. In this study, we propose a feature learning approach using Vision Transformers, which use an attention-based mechanism, and examine the representation learning capability of Transformers as a new backbone architecture for medi…
Scaling Generative Foundation Models for Chest Radiography with Rectified Flow Transformers
Imperial College London（帝国理工学院）；Causality in Healthcare AI Hub（医疗AI因果关系中心）；University of Edinburgh（爱丁堡大学）；Cleveland Clinic London（克利夫兰诊所伦敦）；Department of Perioperative Medicine, CHU Clermont-Ferrand（克莱蒙费朗大学医院围手术期医学科）；Department of Medicine, Massachusetts General Hospital（麻省总医院医学部）；Broad Institute of MIT and Harvard（麻省理工学院与哈佛大学博德研究所）
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）
Chest radiography is the most common radiographic examination performed in daily clinical practice for the detection of various heart and lung abnormalities. The large amount of data to be read and reported, with more than 100 studies per day for a single radiologist, poses a challenge in consistently maintaining high interpretation accuracy. The introduction of large-scale public datasets has led to a series of novel systems for automated abnormality classification. However, the labels of these datasets were obtained using natural language processed medical reports, yielding a large degree of…
Florida International University（佛罗里达国际大学）；Technical University of Munich（慕尼黑工业大学）；Stanford University（斯坦福大学）
Chest X-ray radiography is one of the earliest medical imaging technologies and remains one of the most widely-used for diagnosis, screening, and treatment follow up of diseases related to lungs and heart. The literature in this field of research reports many interesting studies dealing with the challenging tasks of bone suppression and organ segmentation but performed separately, limiting any learning that comes with the consolidation of parameters that could optimize both processes. This study, and for the first time, introduces a multitask deep learning model that generates simultaneously t…
A Generative Foundation Model for Chest Radiography
Department of Radiation Oncology, Stanford University School of Medicine（斯坦福大学医学院放射肿瘤科）；Department of Computer Science, School of Computing and Data Science, The University of Hong Kong（香港大学计算机科学系）；Department of Population Medicine and Diagnostic Sciences, College of Veterinary Medicine, Cornell University（康奈尔大学兽医学院人口医学与诊断科学系）；Department of Radiology, The First Affiliated Hospital of Jinan University（暨南大学第一附属医院放射科）；Department of Radiology, Stanford University School of Medicine（斯坦福大学医学院放射科）；Department of Computer Science and Engineering, The Chinese University of Hong Kong（香港中文大学计算机科学与工程系）；Department of Biomedical Informatics, Harvard Medical School（哈佛医学院生物医学信息学系）
XBench: A Comprehensive Benchmark for Visual-Language Explanations in Chest Radiography
ARTORG Center for Biomedical Engineering Research, University of Bern, Switzerland（ARTORG生物医学工程研究中心，伯尔尼大学，瑞士）；Shanghai Jiao Tong University, China（上海交通大学，中国）；Kaiko.AI, Switzerland（Kaiko.AI，瑞士）；Dept. of Radiation Oncology, Inselspital, Bern University Hospital（放射肿瘤科，因斯普尔茨医院，伯尔尼大学医院）
Beyond the First Read: AI-Assisted Perceptual Error Detection in Chest Radiography Accounting for Interobserver Variability
High School Student（高中学生）；PhD Candidate（博士生）；Department of Electrical and Computer Engineering University of Houston（电气与计算机工程系，休斯顿大学）；Department of Computer Science Emory University（计算机科学系，埃默里大学）；Department of Thoracic Imaging, Division of Diagnostic Imaging The University of Texas MD Anderson Cancer Center, Houston, TX（胸腔成像部，诊断影像学分会，德克萨斯大学MD安德森癌症中心，休斯顿，TX）
National Research Council Canada（加拿大国家研究委员会）；University of Waterloo（滑铁卢大学）；Ford Motor Company（福特汽车公司）
Chest radiography is an effective screening tool for diagnosing pulmonary diseases. In computer-aided diagnosis, extracting the relevant region of interest, i.e., isolating the lung region of each radiography image, can be an essential step towards improved performance in diagnosing pulmonary disorders. Methods: In this work, we propose a deep learning approach to enhance abnormal chest x-ray (CXR) identification performance through segmentations. Our approach is designed in a cascaded manner and incorporates two modules: a deep neural network with criss-cross attention modules (XLSor) for loc…
One of the critical tools for early detection and subsequent evaluation of the incidence of lung diseases is chest radiography. This study presents a real-world implementation of a convolutional neural network (CNN) based Carebot Covid app to detect COVID-19 from chest X-ray (CXR) images. Our proposed model takes the form of a simple and intuitive application. Used CNN can be deployed as a STOW-RS prediction endpoint for direct implementation into DICOM viewers. The results of this study show that the deep learning model based on DenseNet and ResNet architecture can detect SARS-CoV-2 from CXR …
Google Health（谷歌健康）；Advanced Clinical（先进临床）；Apollo Radiology International（阿波罗国际放射学）；Center of Infectious Disease Research in Zambia（赞比亚传染病研究中心）；Sibanye Stillwater（斯班耶-斯蒂尔沃特）；Clickmedix
Tuberculosis (TB) is a top-10 cause of death worldwide. Though the WHO recommends chest radiographs (CXRs) for TB screening, the limited availability of CXR interpretation is a barrier. We trained a deep learning system (DLS) to detect active pulmonary TB using CXRs from 9 countries across Africa, Asia, and Europe, and utilized large-scale CXR pretraining, attention pooling, and noisy student semi-supervised learning. Evaluation was on (1) a combined test set spanning China, India, US, and Zambia, and (2) an independent mining population in South Africa. Given WHO targets of 90% sensitivity an…
Technical University of Kosice（科希策技术大学）；Pavol Jozef Safarik University（帕沃尔·约瑟夫·沙法里克大学）；Louis Pasteur University Hospital（路易·巴斯德大学医院）
Chest radiography is a relatively cheap, widely available medical procedure that conveys key information for making diagnostic decisions. Chest X-rays are almost always used in the diagnosis of respiratory diseases such as pneumonia or the recent COVID-19. In this paper, we propose a self-supervised deep neural network that is pretrained on an unlabeled chest X-ray dataset. The learned representations are transferred to downstream task - the classification of respiratory diseases. The results obtained on four public datasets show that our approach yields competitive results without requiring l…
Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Quick and accurate diagnosis is of paramount importance to mitigate the effects of COVID-19 infection, particularly for severe cases. Enormous effort has been put towards developing deep learning methods to classify and detect COVID-19 infections from chest radiography images. However, recently some questions have been raised surrounding the clinical viability and effectiveness of such methods. In this work, we investigate the impact of multi-task learning (classification and segmentation) on the ability of CNNs to differentiate between various appearances of COVID-19 infections in the lung. W…
Rochester Institute of Technology（罗切斯特理工学院）；IBM Almaden Research Center（IBM阿尔马登研究中心）
Chest radiography is the most common medical image examination for screening and diagnosis in hospitals. Automatic interpretation of chest X-rays at the level of an entry-level radiologist can greatly benefit work prioritization and assist in analyzing a larger population. Subsequently, several datasets and deep learning-based solutions have been proposed to identify diseases based on chest X-ray images. However, these methods are shown to be vulnerable to shift in the source of data: a deep learning model performing well when tested on the same dataset as training data, starts to perform poor…
Warsaw University of Technology（华沙理工大学）；Sano Centre for Computational Medicine（萨诺计算医学中心）；Jagiellonian University（雅盖隆大学）；Tooploox
A critical step in the fight against COVID-19, which continues to have a catastrophic impact on peoples lives, is the effective screening of patients presented in the clinics with severe COVID-19 symptoms. Chest radiography is one of the promising screening approaches. Many studies reported detecting COVID-19 in chest X-rays accurately using deep learning. A serious limitation of many published approaches is insufficient attention paid to explaining decisions made by deep learning models. Using explainable artificial intelligence methods, we demonstrate that model decisions may rely on confoun…
Snap Inc.（Snap 公司）；Isfahan University of Medical Sciences（伊斯法罕医科大学）；The University of Iowa（爱荷华大学）；Isfahan University of Technology（伊斯法罕理工大学）
The COVID-19 pandemic is causing a major outbreak in more than 150 countries around the world, having a severe impact on the health and life of many people globally. One of the crucial step in fighting COVID-19 is the ability to detect the infected patients early enough, and put them under special care. Detecting this disease from radiography and radiology images is perhaps one of the fastest ways to diagnose the patients. Some of the early studies showed specific abnormalities in the chest radiograms of patients infected with COVID-19. Inspired by earlier works, we study the application of de…
National Institutes of Health (NIH) Clinical Center（美国国立卫生研究院临床中心）；Ping An Technology（平安科技）；Ping An Technology Co., Ltd.（平安科技有限公司）
Being one of the most common diagnostic imaging tests, chest radiography requires timely reporting of potential findings in the images. In this paper, we propose an end-to-end architecture for abnormal chest X-ray identification using generative adversarial one-class learning. Unlike previous approaches, our method takes only normal chest X-ray images as input. The architecture is composed of three deep neural networks, each of which learned by competing while collaborating among them to model the underlying content structure of the normal chest X-rays. Given a chest X-ray image in the testing…
Enhancing Representation in Radiography-Reports Foundation Model: A Granular Alignment Algorithm Using Masked Contrastive Learning
Paul C. Lauterbur Research Center for Biomedical Imaging（保罗·C·劳特伯生物医学成像研究中心）；Shenzhen Institute of Advanced Technology（深圳先进技术研究院）；Pengcheng Laboratory（鹏城实验室）；University of Chinese Academy of Sciences（中国科学院大学）；Harvard University（哈佛大学）；Shanghai AI Lab（上海人工智能实验室）
University Medical Center Hamburg-Eppendorf（汉堡-埃彭多夫大学医学中心）；Hamburg University of Technology（汉堡工业大学）；Forschungszentrum Medizintechnik Hamburg（汉堡医学技术研究中心）；Philips Research（飞利浦研究院）
Chest radiography is the most common clinical examination type. To improve the quality of patient care and to reduce workload, methods for automatic pathology classification have been developed. In this contribution we investigate the usefulness of two advanced image pre-processing techniques, initially developed for image reading by radiologists, for the performance of Deep Learning methods. First, we use bone suppression, an algorithm to artificially remove the rib cage. Secondly, we employ an automatic lung field detection to crop the image to the lung area. Furthermore, we consider the com…
Microsoft Research（微软研究院）；University Hospitals Birmingham（伯明翰大学医院）
Chest radiography has been a recommended procedure for patient triaging and resource management in intensive care units (ICUs) throughout the COVID-19 pandemic. The machine learning efforts to augment this workflow have been long challenged due to deficiencies in reporting, model evaluation, and failure mode analysis. To address some of those shortcomings, we model radiological features with a human-interpretable class hierarchy that aligns with the radiological decision process. Also, we propose the use of a data-driven error analysis methodology to uncover the blind spots of our model, provi…
National Research Council Canada（加拿大国家研究委员会）；University of Waterloo（滑铁卢大学）
Effective representation learning is the key in improving model performance for medical image analysis. In training deep learning models, a compromise often must be made between performance and trust, both of which are essential for medical applications. Moreover, models optimized with cross-entropy loss tend to suffer from unwarranted overconfidence in the majority class and over-cautiousness in the minority class. In this work, we integrate a new surrogate loss with self-supervised learning for computer-aided screening of COVID-19 patients using radiography images. In addition, we adopt a ne…
Harrisburg University of Science and Technology（哈里斯堡科技大学）
Pneumonia is caused by viruses, bacteria, or fungi that infect the lungs, which, if not diagnosed, can be fatal and lead to respiratory failure. More than 250,000 individuals in the United States, mainly adults, are diagnosed with pneumonia each year, and 50,000 die from the disease. Chest Radiography (X-ray) is widely used by radiologists to detect pneumonia. It is not uncommon to overlook pneumonia detection for a well-trained radiologist, which triggers the need for improvement in the diagnosis's accuracy. In this work, we propose using transfer learning, which can reduce the neural network…
Prince Sultan University（苏丹王子大学）；University of Manouba（马努巴大学）
Deep learning (DL) is being increasingly utilized in healthcare-related fields due to its outstanding efficiency. However, we have to keep the individual health data used by DL models private and secure. Protecting data and preserving the privacy of individuals has become an increasingly prevalent issue. The gap between the DL and privacy communities must be bridged. In this paper, we propose privacy-preserving deep learning (PPDL)-based approach to secure the classification of Chest X-ray images. This study aims to use Chest X-ray images to their fullest potential without compromising the pri…
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…
Digital Technologies Research Centre, National Research Council of Canada（加拿大国家研究委员会数字技术研究中心）；University of Waterloo（滑铁卢大学）
The COVID-19 pandemic has had devastating effects on the well-being of the global population. The pandemic has been so prominent partly due to the high infection rate of the virus and its variants. In response, one of the most effective ways to stop infection is rapid diagnosis. The main-stream screening method, reverse transcription-polymerase chain reaction (RT-PCR), is time-consuming, laborious and in short supply. Chest radiography is an alternative screening method for the COVID-19 and computer-aided diagnosis (CAD) has proven to be a viable solution at low cost and with fast speed; howev…
New York University（纽约大学）；Manipal Institute of Technology, Manipal Academy of Higher Education（马尼帕尔高等教育学院马尼帕尔理工学院）；Columbia University（哥伦比亚大学）
Biomedical image segmentation is one of the fastest growing fields which has seen extensive automation through the use of Artificial Intelligence. This has enabled widespread adoption of accurate techniques to expedite the screening and diagnostic processes which would otherwise take several days to finalize. In this paper, we present an end-to-end pipeline to segment lungs from chest X-ray images, training the neural network model on the Japanese Society of Radiological Technology (JSRT) dataset, using UNet to enable faster processing of initial screening for various lung disorders. The pipel…
National Cheng Kung University（国立成功大学）；National Cheng Kung University Hospital（国立成功大学附属医院）
The image quality of portable supine chest radiographs is inherently poor due to low contrast and high noise. The endotracheal intubation detection requires the locations of the endotracheal tube (ETT) tip and carina. The goal is to find the distance between the ETT tip and the carina in chest radiography. To overcome such a problem, we propose a feature extraction method with Mask R-CNN. The Mask R-CNN predicts a tube and a tracheal bifurcation in an image. Then, the feature extraction method is used to find the feature point of the ETT tip and that of the carina. Therefore, the ETT-carina di…
Université de Haute-Alsace（上阿尔萨斯大学）；Université de Strasbourg（斯特拉斯堡大学）；University of Lille（里尔大学）；YNCREA-ISEN Lille（里尔YNCREA-ISEN工程学院）；University of the Basque Country（巴斯克大学）；IKERBASQUE, Basque Foundation for Science（伊克尔巴斯克巴斯克科学基金会）；The University of Tokyo（东京大学）；CNRS（法国国家科学研究中心）；Inserm（法国国家健康与医学研究院）；CHU Lille（里尔大学医院）
Coronavirus disease 2019 (COVID-19) is an infectious disease with first symptoms similar to the flu. COVID-19 appeared first in China and very quickly spreads to the rest of the world, causing then the 2019-20 coronavirus pandemic. In many cases, this disease causes pneumonia. Since pulmonary infections can be observed through radiography images, this paper investigates deep learning methods for automatically analyzing query chest X-ray images with the hope to bring precision tools to health professionals towards screening the COVID-19 and diagnosing confirmed patients. In this context, traini…
Carnegie Mellon University（卡内基梅隆大学）；University of Pennsylvania（宾夕法尼亚大学）
We present a weakly supervised deep learning model for classifying thoracic diseases and identifying abnormalities in chest radiography. In this work, instead of learning from medical imaging data with region-level annotations, our model was merely trained on imaging data with image-level labels to classify diseases, and is able to identify abnormal image regions simultaneously. Our model consists of a customized pooling structure and an adaptive DenseNet front-end, which can effectively recognize possible disease features for classification and localization tasks. Our method has been validate…
Toronto Rehabilitation Institute, University Health Network（多伦多康复研究所，大学健康网络）；University of Toronto（多伦多大学）；University of Manitoba（曼尼托巴大学）
The current COVID-19 pandemic is now getting contained, albeit at the cost of morethan2.3million human lives. A critical phase in any pandemic is the early detection of cases to develop preventive treatments and strategies. In the case of COVID-19,several studies have indicated that chest radiography images of the infected patients show characteristic abnormalities. However, at the onset of a given pandemic, such asCOVID-19, there may not be sufficient data for the affected cases to train models for their robust detection. Hence, supervised classification is ill-posed for this problem because …
Epanechnikov nonparametric kernel density estimation based feature-learning in respiratory disease chest X-ray images
Department of Data Science, Data Science and AI Laboratory（数据科学系）；Catholic University of Argentina (UCA)（阿根廷天主教大学）；MACS School（MACS学院）；Heriot-Watt University（赫罗特-瓦特大学）
Purpose: Considering several patients screened due to COVID-19 pandemic, computer-aided detection has strong potential in assisting clinical workflow efficiency and reducing the incidence of infections among radiologists and healthcare providers. Since many confirmed COVID-19 cases present radiological findings of pneumonia, radiologic examinations can be useful for fast detection. Therefore, chest radiography can be used to fast screen COVID-19 during the patient triage, thereby determining the priority of patient's care to help saturated medical facilities in a pandemic situation. Methods: I…
UT Austin（得克萨斯大学奥斯汀分校）；MIS, Germany（德国曼海姆应用科学大学）；Weill Cornell Medicine（威尔康奈尔医学院）
Computer-aided diagnosis plays a salient role in more accessible and accurate cardiopulmonary diseases classification and localization on chest radiography. Millions of people get affected and die due to these diseases without an accurate and timely diagnosis. Recently proposed contrastive learning heavily relies on data augmentation, especially positive data augmentation. However, generating clinically-accurate data augmentations for medical images is extremely difficult because the common data augmentation methods in computer vision, such as sharp, blur, and crop operations, can severely alt…
Google Health, Google（谷歌健康，谷歌）；Apollo Radiology International（阿波罗国际放射医学）；Northwestern Medicine（西北医学）
Chest radiography (CXR) is the most widely-used thoracic clinical imaging modality and is crucial for guiding the management of cardiothoracic conditions. The detection of specific CXR findings has been the main focus of several artificial intelligence (AI) systems. However, the wide range of possible CXR abnormalities makes it impractical to build specific systems to detect every possible condition. In this work, we developed and evaluated an AI system to classify CXRs as normal or abnormal. For development, we used a de-identified dataset of 248,445 patients from a multi-city hospital networ…
The novel corona virus (Covid-19) has introduced significant challenges due to its rapid spreading nature through respiratory transmission. As a result, there is a huge demand for Artificial Intelligence (AI) based quick disease diagnosis methods as an alternative to high demand tests such as Polymerase Chain Reaction (PCR). Chest X-ray (CXR) Image analysis is such cost-effective radiography technique due to resource availability and quick screening. But, a sufficient and systematic data collection that is required by complex deep leaning (DL) models is more difficult and hence there are recen…
Fraunhofer FIT（弗劳恩霍夫应用信息技术研究所）；RWTH Aachen University（亚琛工业大学）；German National Library of Medicine（德国国家医学图书馆）；University of Cologne（科隆大学）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）
Amid the coronavirus disease(COVID-19) pandemic, humanity experiences a rapid increase in infection numbers across the world. Challenge hospitals are faced with, in the fight against the virus, is the effective screening of incoming patients. One methodology is the assessment of chest radiography(CXR) images, which usually requires expert radiologist's knowledge. In this paper, we propose an explainable deep neural networks(DNN)-based method for automatic detection of COVID-19 symptoms from CXR images, which we call DeepCOVIDExplainer. We used 15,959 CXR images of 15,854 patients, covering nor…
Less Could Be Better: Parameter-efficient Fine-tuning Advances Medical Vision Foundation Models
School of Informatics, Xiamen University（厦门大学信息学院）；Department of Biomedical Informatics, Harvard University（哈佛大学生物医学信息学系）；Department of Computer Science, The University of Hong Kong（香港大学计算机科学系）
本文实证评估 LoRA 对胸部 X 线摄影自监督基础模型的微调效果，证明其以不足 1% 参数在多数任务上超过全参数微调，并刷新数据高效学习基准。
Dartmouth College（达特茅斯学院）；Dartmouth-Hitchcock Medical Center（达特茅斯-希区柯克医疗中心）
Background: Deep learning has great potential to assist with detecting and triaging critical findings such as pneumoperitoneum on medical images. To be clinically useful, the performance of this technology still needs to be validated for generalizability across different types of imaging systems. Materials and Methods: This retrospective study included 1,287 chest X-ray images of patients who underwent initial chest radiography at 13 different hospitals between 2011 and 2019. The chest X-ray images were labelled independently by four radiologist experts as positive or negative for pneumoperito…
Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New Benchmark Study
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Texas A&M University（德克萨斯农工大学）；National Institutes of Health（美国国立卫生研究院）；Weill Cornell Medicine（威尔康奈尔医学院）
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
In this era of pandemic, the future of healthcare industry has never been more exciting. Artificial intelligence and machine learning (AI & ML) present opportunities to develop solutions that cater for very specific needs within the industry. Deep learning in healthcare had become incredibly powerful for supporting clinics and in transforming patient care in general. Deep learning is increasingly being applied for the detection of clinically important features in the images beyond what can be perceived by the naked human eye. Chest X-ray images are one of the most common clinical method for di…
