Although the field of sleep study has greatly developed over the recent years, the most common and efficient way to detect sleep issues remains a sleep examination performed in a sleep laboratory, in a procedure called Polysomnography (PSG). This examination measures several vital signals during a full night's sleep using multiple sensors connected to the patient's body. Yet, despite being the golden standard, the connection of the sensors and the unfamiliar environment inevitably impact the quality of the patient's sleep and the examination itself. Therefore, with the novel development of more accurate and affordable 3D sensing devices, new approaches for non-contact sleep study emerged. These methods utilize different techniques with the purpose to extract the same sleep parameters, but remotely, eliminating the need of any physical connections to the patient's body. However, in order to enable reliable remote extraction, these methods require accurate identification of the basic Region of Interest (ROI) i.e. the chest area of the patient, a task that is currently holding back the development process, as it is performed manually for each patient. In this study, we propose an automatic chest area segmentation algorithm, that given an input set of 3D frames of a sleeping patient, outputs a segmentation image with the pixels that correspond to the chest area, and can then be used as an input to subsequent sleep analysis algorithms. Except for significantly speeding up the development process of the non-contact methods, accurate automatic segmentation can also enable a more precise feature extraction and it is shown it is already improving sensitivity of prior solutions on average 46.9% better compared to manual ROI selection. All mentioned will place the extraction algorithms of the non-contact methods as a leading candidate to replace the existing traditional methods used today.
Fraunhofer IAIS（弗劳恩霍夫智能分析与信息系统研究所）；University of Bonn（波恩大学）；University Hospital Bonn（波恩大学医院）
Chest X-Ray imaging is one of the most common radiological tools for detection of various pathologies related to the chest area and lung function. In a clinical setting, automated assessment of chest radiographs has the potential of assisting physicians in their decision making process and optimize clinical workflows, for example by prioritizing emergency patients. Most work analyzing the potential of machine learning models to classify chest X-ray images focuses on vision methods processing and predicting pathologies for one image at a time. However, many patients undergo such a procedure mul…
Recent developments in medical imaging with Deep Learning presents evidence of automated diagnosis and prognosis. It can also be a complement to currently available diagnosis methods. Deep Learning can be leveraged for diagnosis, severity prediction, intubation support prediction and many similar tasks. We present prediction of intubation support requirement for patients from the Chest X-ray using Deep representation learning. We release our source code publicly at https://github.com/aniketmaurya/covid-research.
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…
Bootstrapping Chest CT Image Understanding by Distilling Knowledge from X-ray Expert Models
DAMO Academy, Alibaba Group（阿里巴巴达摩院）；University of Science and Technology of China（中国科学技术大学）；Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences（中国科学院苏州生物医学工程技术研究所）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；The First Affiliated Hospital of College of Medicine, Zhejiang University（浙江大学医学院附属第一医院）
针对胸部 CT 缺乏大规模多病种标注数据的问题，本文提出从预训练 2D X-ray 专家模型蒸馏诊断知识，并结合语言引导检索与鲁棒对比学习，提升 3D CT 理解和报告生成能力。
IBM Research – Almaden Research Center（IBM研究院阿尔马登研究中心）
Chest X-rays (CXRs) are among the most commonly used medical image modalities. They are mostly used for screening, and an indication of disease typically results in subsequent tests. As this is mostly a screening test used to rule out chest abnormalities, the requesting clinicians are often interested in whether a CXR is normal or not. A machine learning algorithm that can accurately screen out even a small proportion of the "real normal" exams out of all requested CXRs would be highly beneficial in reducing the workload for radiologists. In this work, we report a deep neural network trained f…
The University of British Columbia（不列颠哥伦比亚大学）；Xtract AI；Element AI；SapienML；AWS, Worldwide Public Sector（亚马逊云科技全球公共部门）
The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has rapidly spread into a global pandemic. A form of pneumonia, presenting as opacities with in a patient's lungs, is the most common presentation associated with this virus, and great attention has gone into how these changes relate to patient morbidity and mortality. In this work we provide open source models for the segmentation of patterns of pulmonary opacification on chest Computed Tomography (CT) scans which have been correlated with various stages and severities of infection. We have collected 663 chest CT scans of COVID-…
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…
University of Oklahoma（俄克拉荷马大学）；University of Memphis（孟菲斯大学）；Marquette University（马凯特大学）
The outbreak of COVID-19 disease caused more than 100,000 deaths so far in the USA alone. It is necessary to conduct an initial screening of patients with the symptoms of COVID-19 disease to control the spread of the disease. However, it is becoming laborious to conduct the tests with the available testing kits due to the growing number of patients. Some studies proposed CT scan or chest X-ray images as an alternative solution. Therefore, it is essential to use every available resource, instead of either a CT scan or chest X-ray to conduct a large number of tests simultaneously. As a result, t…
With the rise and ever-increasing potential of deep learning techniques in recent years, publicly available medical datasets became a key factor to enable reproducible development of diagnostic algorithms in the medical domain. Medical data contains sensitive patient-related information and is therefore usually anonymized by removing patient identifiers, e.g., patient names before publication. To the best of our knowledge, we are the first to show that a well-trained deep learning system is able to recover the patient identity from chest X-ray data. We demonstrate this using the publicly avail…
University of Warwick（华威大学）；Guy’s and St Thomas’ NHS Foundation Trust（盖伊和圣托马斯国民保健服务基金会信托）；University Hospitals Coventry and Warwickshire NHS Trust（考文垂与沃里克郡大学医院国民保健服务信托）；Guy’s and St Thomas’ NHS Trust（盖伊和圣托马斯国民保健服务信托）；King’s College London（伦敦国王学院）；The Alan Turing Institute（艾伦·图灵研究所）
Understanding the internal physiological changes accompanying the aging process is an important aspect of medical image interpretation, with the expected changes acting as a baseline when reporting abnormal findings. Deep learning has recently been demonstrated to allow the accurate estimation of patient age from chest X-rays, and shows potential as a health indicator and mortality predictor. In this paper we present a novel comparative study of the relative performance of radiologists versus state-of-the-art deep learning models on two tasks: (a) patient age estimation from a single chest X-r…
Indian Institute of Technology Delhi（德里印度理工学院）；University of Bath（巴斯大学）；Indian Institute of Technology Kanpur（坎普尔印度理工学院）；All India Institute of Medical Sciences Delhi（德里全印度医学科学研究所）
The exponential increase in COVID-19 patients is overwhelming healthcare systems across the world. With limited testing kits, it is impossible for every patient with respiratory illness to be tested using conventional techniques (RT-PCR). The tests also have long turn-around time, and limited sensitivity. Detecting possible COVID-19 infections on Chest X-Ray may help quarantine high risk patients while test results are awaited. X-Ray machines are already available in most healthcare systems, and with most modern X-Ray systems already digitized, there is no transportation time involved for the …
The use of ImageNet pre-trained networks is becoming widespread in the medical imaging community. It enables training on small datasets, commonly available in medical imaging tasks. The recent emergence of a large Chest X-ray dataset opened the possibility for learning features that are specific to the X-ray analysis task. In this work, we demonstrate that the features learned allow for better classification results for the problem of Tuberculosis detection and enable generalization to an unseen dataset. To accomplish the task of feature learning, we train a DenseNet-121 CNN on 112K images fro…
Charles Sturt University（查尔斯特大学）；University of Technology Sydney（悉尼科技大学）；Wollongong Hospital（伍伦贡医院）
The application of computer vision for COVID-19 diagnosis is complex and challenging, given the risks associated with patient misclassifications. Arguably, the primary value of medical imaging for COVID-19 lies rather on patient prognosis. Radiological images can guide physicians assessing the severity of the disease, and a series of images from the same patient at different stages can help to gauge disease progression. Hence, a simple method based on lung-pathology interpretable features for scoring disease severity from Chest X-rays is proposed here. As the primary contribution, this method …
Wayne State Univ.（韦恩州立大学）；NanoBioScience Institute, Wayne State Univ.（韦恩州立大学纳米生物科学研究所）；University of Michigan（密歇根大学）；Henry Ford Health System（亨利福特医疗系统）
Deep Neural Networks (DNN) are widely used to carry out segmentation tasks in biomedical images. Most DNNs developed for this purpose are based on some variation of the encoder-decoder U-Net architecture. Here we show that Res-CR-Net, a new type of fully convolutional neural network, which was originally developed for the semantic segmentation of microscopy images, and which does not adopt a U-Net architecture, is very effective at segmenting the lung fields in chest X-rays from either healthy patients or patients with a variety of lung pathologies.
Stanford University（斯坦福大学）；School of Medicine, Stanford University（斯坦福大学医学院）
Self-supervised contrastive learning between pairs of multiple views of the same image has been shown to successfully leverage unlabeled data to produce meaningful visual representations for both natural and medical images. However, there has been limited work on determining how to select pairs for medical images, where availability of patient metadata can be leveraged to improve representations. In this work, we develop a method to select positive pairs coming from views of possibly different images through the use of patient metadata. We compare strategies for selecting positive pairs for ch…
Saratoga High School（萨拉托加高中）；Stanford University（斯坦福大学）
Chest X-rays of coronavirus disease 2019 (COVID-19) patients are frequently obtained to determine the extent of lung disease and are a valuable source of data for creating artificial intelligence models. Most work to date assessing disease severity on chest imaging has focused on segmenting computed tomography (CT) images; however, given that CTs are performed much less frequently than chest X-rays for COVID-19 patients, automated lung lesion segmentation on chest X-rays could be clinically valuable. There currently exists a universal shortage of chest X-rays with ground truth COVID-19 lung le…
University of Waterloo（滑铁卢大学）；DarwinAI Corp.（DarwinAI公司）；Niagara Health（尼亚加拉医疗）；McMaster University（麦克马斯特大学）；Thunder Bay Regional Health Sciences Centre（桑德贝地区健康科学中心）；Northern Ontario School of Medicine（北部安大略医学院）
The world is still struggling in controlling and containing the spread of the COVID-19 pandemic caused by the SARS-CoV-2 virus. The medical conditions associated with SARS-CoV-2 infections have resulted in a surge in the number of patients at clinics and hospitals, leading to a significantly increased strain on healthcare resources. As such, an important part of managing and handling patients with SARS-CoV-2 infections within the clinical workflow is severity assessment, which is often conducted with the use of chest x-ray (CXR) images. In this work, we introduce COVID-Net CXR-S, a convolution…
Chest X-ray is the most common medical imaging exam used to assess multiple pathologies. Automated algorithms and tools have the potential to support the reading workflow, improve efficiency, and reduce reading errors. With the availability of large scale data sets, several methods have been proposed to classify pathologies on chest X-ray images. However, most methods report performance based on random image based splitting, ignoring the high probability of the same patient appearing in both training and test set. In addition, most methods fail to explicitly incorporate the spatial information…
Longitudinal Data and a Semantic Similarity Reward for Chest X-Ray Report Generation
The Australian e-Health Research Centre, CSIRO Health and Biosecurity（澳大利亚联邦科学与工业研究组织健康与生物安全部澳大利亚电子健康研究中心）；School of Electrical Engineering & Computer Science, University of Queensland（昆士兰大学电气工程与计算机科学学院）；Imaging Associates（影像协会）；St Vincent’s Hospital（圣文森特医院）；Monash Health（莫纳什医疗中心）
SARS-CoV-2, also known as COVID-19 or Coronavirus, is a viral contagious disease that is infected by a novel coronavirus, and has been rapidly spreading across the globe. It is very important to test and isolate people to reduce spread, and from here comes the need to do this quickly and efficiently. According to some studies, Chest-CT outperforms RT-PCR lab testing, which is the current standard, when diagnosing COVID-19 patients. Due to this, computer vision researchers have developed various deep learning systems that can predict COVID-19 using a Chest-CT scan correctly to a certain degree.…
During the COVID-19 pandemic, there has been an emerging need for rapid, dedicated, and point-of-care COVID-19 patient disposition techniques to optimize resource utilization and clinical workflow. In view of this need, we present COVID-MobileXpert: a lightweight deep neural network (DNN) based mobile app that can use chest X-ray (CXR) for COVID-19 case screening and radiological trajectory prediction. We design and implement a novel three-player knowledge transfer and distillation (KTD) framework including a pre-trained attending physician (AP) network that extracts CXR imaging features from …
National Yang Ming Chiao Tung University（阳明交通大学）；National Cheng Kung University Hospital（成功大学附属医院）；National Cheng Kung University（成功大学）
The purpose of this research is to develop a system that generates simulated computed tomography pulmonary angiography (CTPA) images clinically for pulmonary embolism diagnoses. Nowadays, CTPA images are the gold standard computerized detection method to determine and identify the symptoms of pulmonary embolism (PE), although performing CTPA is harmful for patients and also expensive. Therefore, we aim to detect possible PE patients through CT images. The system will simulate CTPA images with deep learning models for the identification of PE patients' symptoms, providing physicians with anothe…
University of Wisconsin（威斯康星大学）；Henry Ford Health（亨利福特医疗集团）
Purpose: To answer the long-standing question of whether a model trained from a single clinical site can be generalized to external sites. Materials and Methods: 17,537 chest x-ray radiographs (CXRs) from 3,264 COVID-19-positive patients and 4,802 COVID-19-negative patients were collected from a single site for AI model development. The generalizability of the trained model was retrospectively evaluated using four different real-world clinical datasets with a total of 26,633 CXRs from 15,097 patients (3,277 COVID-19-positive patients). The area under the receiver operating characteristic curve…
EVOKE: Elevating Chest X-ray Report Generation via Multi-View Contrastive Learning and Patient-Specific Knowledge
Xidian University（西安电子科技大学）；Shanghai Jiao Tong University School of Medicine（上海交通大学医学院）；Ruijin Hospital（瑞金医院）；Shanghai Institute of Traumatology and Orthopedics（上海市伤骨科研究所）；Harbin Medical University（哈尔滨医科大学）；The First Affiliated Hospital of Harbin Medical University（哈尔滨医科大学附属第一医院）；Brown University（布朗大学）
Age prediction based on appearances of different anatomies in medical images has been clinically explored for many decades. In this paper, we used deep learning to predict a persons age on Chest X-Rays. Specifically, we trained a CNN in regression fashion on a large publicly available dataset. Moreover, for interpretability, we explored activation maps to identify which areas of a CXR image are important for the machine (i.e. CNN) to predict a patients age, offering insight. Overall, amongst correctly predicted CXRs, we see areas near the clavicles, shoulders, spine, and mediastinum being most…
Chest radiographs are frequently used to verify the correct intubation of patients in the emergency room. Fast and accurate identification and localization of the endotracheal (ET) tube is critical for the patient. In this study we propose a novel automated deep learning scheme for accurate detection and segmentation of the ET tubes. Development of automatic systems using deep learning networks for classification and segmentation require large annotated data which is not always available. Here we present an approach for synthesizing ET tubes in real X-ray images. We suggest a method for traini…
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…
VRVis Zentrum für Virtual Reality und Visualisierung Forschungs-GmbH（VRVis虚拟现实与可视化研究中心有限公司）；Agfa NV（爱克发股份有限公司）；Antwerp University Hospital（安特卫普大学医院）；University of Antwerp（安特卫普大学）
To facilitate both the detection and the interpretation of findings in chest X-rays, comparison with a previous image of the same patient is very valuable to radiologists. Today, the most common approach for deep learning methods to automatically inspect chest X-rays disregards the patient history and classifies only single images as normal or abnormal. Nevertheless, several methods for assisting in the task of comparison through image registration have been proposed in the past. However, as we illustrate, they tend to miss specific types of pathological changes like cardiomegaly and effusion.…
The University of Texas at Arlington（德克萨斯大学阿灵顿分校）；Tencent AI Lab（腾讯人工智能实验室）
Chest X-rays is one of the most commonly available and affordable radiological examinations in clinical practice. While detecting thoracic diseases on chest X-rays is still a challenging task for machine intelligence, due to 1) the highly varied appearance of lesion areas on X-rays from patients of different thoracic disease and 2) the shortage of accurate pixel-level annotations by radiologists for model training. Existing machine learning methods are unable to deal with the challenge that thoracic diseases usually happen in localized disease-specific areas. In this article, we propose a weak…
Expert Uncertainty and Severity Aware Chest X-Ray Classification by Multi-Relationship Graph Learning
University of Texas at Arlington（德克萨斯大学阿灵顿分校）；RIKEN（理化学研究所）；University of Tokyo（东京大学）；National Institute of Health Clinical Center（美国国立卫生研究院临床中心）；National Cancer Center Research Institute（国立癌症研究中心研究所）
Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Daimler TSS（戴姆勒TSS公司）；University Hospital Heidelberg（海德堡大学医院）；German Cancer Research Center（德国癌症研究中心）；University Clinic Essen（埃森大学医院）
In clinical radiology reports, doctors capture important information about the patient's health status. They convey their observations from raw medical imaging data about the inner structures of a patient. As such, formulating reports requires medical experts to possess wide-ranging knowledge about anatomical regions with their normal, healthy appearance as well as the ability to recognize abnormalities. This explicit grasp on both the patient's anatomy and their appearance is missing in current medical image-processing systems as annotations are especially difficult to gather. This renders th…
CONNECT Centre, Trinity College Dublin（都柏林圣三一大学CONNECT中心）；University of Sindh（信德大学）；Signy Advanced Technologies（西格尼先进技术公司）；Indian Institute of Technology, Dehli（印度理工学院德里分校）；King Faisal University（费萨尔国王大学）
The current COVID-19 pandemic has motivated the researchers to use artificial intelligence techniques for a potential alternative to reverse transcription-polymerase chain reaction (RT-PCR) due to the limited scale of testing. The chest X-ray (CXR) is one of the alternatives to achieve fast diagnosis but the unavailability of large-scale annotated data makes the clinical implementation of machine learning-based COVID detection difficult. Another issue is the usage of ImageNet pre-trained networks which does not extract reliable feature representations from medical images. In this paper, we pro…
National Institute of Technology Delhi（德里国家理工学院）；Indian Institute of Technology, Roorkee（鲁尔基印度理工学院）
Background and Objective: During pandemics, the use of artificial intelligence (AI) approaches combined with biomedical science play a significant role in reducing the burden on the healthcare systems and physicians. The rapid increment in cases of COVID-19 has led to an increase in demand for hospital beds and other medical equipment. However, since medical facilities are limited, it is recommended to diagnose patients as per the severity of the infection. Keeping this in mind, we share our research in detecting COVID-19 as well as assessing its severity using chest-CT scans and Deep Learning…
COVID-19, also known as Novel Coronavirus Disease, is a highly contagious disease that first surfaced in China in late 2019. SARS-CoV-2 is a coronavirus that belongs to the vast family of coronaviruses that causes this disease. The sickness originally appeared in Wuhan, China in December 2019 and quickly spread to over 213 nations, becoming a global pandemic. Fever, dry cough, and tiredness are the most typical COVID-19 symptoms. Aches, pains, and difficulty breathing are some of the other symptoms that patients may face. The majority of these symptoms are indicators of respiratory infections …
Zonguldak Bulent Ecevit University（宗古尔达克·比伦特·埃杰维特大学）
Covid-19 is a very serious deadly disease that has been announced as a pandemic by the world health organization (WHO). The whole world is working with all its might to end Covid-19 pandemic, which puts countries in serious health and economic problems, as soon as possible. The most important of these is to correctly identify those who get the Covid-19. Methods and approaches to support the reverse transcription polymerase chain reaction (RT-PCR) test have begun to take place in the literature. In this study, chest X-ray images, which can be accessed easily and quickly, were used because the c…
Qatar University（卡塔尔大学）；North South University（北南大学）；University of South Carolina（南卡罗来纳大学）；Hamad General Hospital（哈马德综合医院）；AFG College with the University of Aberdeen（阿伯丁大学AFG学院）
Fast and accurate detection of the disease can significantly help in reducing the strain on the healthcare facility of any country to reduce the mortality during any pandemic. The goal of this work is to create a multimodal system using a novel machine learning framework that uses both Chest X-ray (CXR) images and clinical data to predict severity in COVID-19 patients. In addition, the study presents a nomogram-based scoring technique for predicting the likelihood of death in high-risk patients. This study uses 25 biomarkers and CXR images in predicting the risk in 930 COVID-19 patients admitt…
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…
University Campus Bio-Medico of Rome（罗马生物医学自由大学）；Centro Diagnostico Italiano S.p.A.（意大利诊断中心股份公司）；Istituto Italiano di Tecnologia（意大利理工学院）；Bracco Imaging S.p.A.（博莱科影像股份公司）；Sapienza University of Rome（罗马大学）；Fondazione IRCCS Policlinico San Matteo（圣马泰奥综合医院IRCCS基金会）
Recent epidemiological data report that worldwide more than 53 million people have been infected by SARS-CoV-2, resulting in 1.3 million deaths. The disease has been spreading very rapidly and few months after the identification of the first infected, shortage of hospital resources quickly became a problem. In this work we investigate whether chest X-ray (CXR) can be used as a possible tool for the early identification of patients at risk of severe outcome, like intensive care or death. CXR is a radiological technique that compared to computed tomography (CT) it is simpler, faster, more widesp…
MDF-Net for abnormality detection by fusing X-rays with clinical data
Queensland University of Technology（昆士兰科技大学）；Lusíadas Knowledge Center（卢西亚达斯知识中心）；Instituto de Sistemas e Robótica（系统与机器人研究所）；Instituto Superior Técnico, University of Lisbon（里斯本大学高等技术学院）；INESC-ID；Data61 CSIRO
Kyoto University Graduate School of Medicine（京都大学医学研究科）
Rationale and objectives: Several studies have evaluated the usefulness of deep learning for lung segmentation using chest x-ray (CXR) images with small- or medium-sized abnormal findings. Here, we built a database including both CXR images with severe abnormalities and experts' lung segmentation results, and aimed to evaluate our network's efficacy in lung segmentation from these images. Materials and Methods: For lung segmentation, CXR images from the Japanese Society of Radiological Technology (JSRT, N = 247) and Montgomery databases (N = 138), were included, and 65 additional images depict…
In a worldwide health crisis as exigent as COVID-19, there has become a pressing need for rapid, reliable diagnostics. Currently, popular testing methods such as reverse transcription polymerase chain reaction (RT-PCR) can have high false negative rates. Consequently, COVID-19 patients are not accurately identified nor treated quickly enough to prevent transmission of the virus. However, the recent rise of medical CT data has presented promising avenues, since CT manifestations contain key characteristics indicative of COVID-19. This study aimed to take a novel approach in the machine learning…
University of Waterloo（滑铁卢大学）；DarwinAI Corp.（达尔文人工智能公司）
The coronavirus disease 2019 (COVID-19) pandemic continues to have a tremendous impact on patients and healthcare systems around the world. In the fight against this novel disease, there is a pressing need for rapid and effective screening tools to identify patients infected with COVID-19, and to this end CT imaging has been proposed as one of the key screening methods which may be used as a complement to RT-PCR testing, particularly in situations where patients undergo routine CT scans for non-COVID-19 related reasons, patients with worsening respiratory status or developing complications tha…
University of Chittagong（吉大港大学）；Charles Sturt University（查尔斯特大学）
The COVID-19 pandemic continues to severely undermine the prosperity of the global health system. To combat this pandemic, effective screening techniques for infected patients are indispensable. There is no doubt that the use of chest X-ray images for radiological assessment is one of the essential screening techniques. Some of the early studies revealed that the patient's chest X-ray images showed abnormalities, which is natural for patients infected with COVID-19. In this paper, we proposed a parallel-dilated convolutional neural network (CNN) based COVID-19 detection system from chest x-ray…
Detection of subclinical atherosclerosis by image-based deep learning on chest x-ray
Città della Salute e della Scienza Hospital（健康与科学城医院）；University of Turin（都灵大学）；Civil Hospital（平民医院）；Candiolo Cancer Institute, FPO-IRCCS（坎迪奥洛癌症研究所，FPO-IRCCS）；Ospedale San Giovanni Bosco（圣乔瓦尼·博斯科医院）
A major obstacle to the integration of deep learning models for chest x-ray interpretation into clinical settings is the lack of understanding of their failure modes. In this work, we first investigate whether there are patient subgroups that chest x-ray models are likely to misclassify. We find that patient age and the radiographic finding of lung lesion, pneumothorax or support devices are statistically relevant features for predicting misclassification for some chest x-ray models. Second, we develop misclassification predictors on chest x-ray models using their outputs and clinical features…
University of Michigan（密歇根大学）；Institute for Healthcare Policy & Innovation（医疗政策与创新研究所）；Department of Electrical Engineering and Computer Science（电气工程与计算机科学系）；Department of Radiology（放射科）；Department of Internal Medicine（内科）
While deep learning has shown promise in improving the automated diagnosis of disease based on chest X-rays, deep networks may exhibit undesirable behavior related to shortcuts. This paper studies the case of spurious class skew in which patients with a particular attribute are spuriously more likely to have the outcome of interest. For instance, clinical protocols might lead to a dataset in which patients with pacemakers are disproportionately likely to have congestive heart failure. This skew can lead to models that take shortcuts by heavily relying on the biased attribute. We explore this p…
University of Alicante（阿利坎特大学）；Hospital San Juan de Alicante（阿利坎特圣胡安医院）；Regional Ministry of Health, Valencia（瓦伦西亚地区卫生部）
We present a labeled large-scale, high resolution chest x-ray dataset for the automated exploration of medical images along with their associated reports. This dataset includes more than 160,000 images obtained from 67,000 patients that were interpreted and reported by radiologists at Hospital San Juan Hospital (Spain) from 2009 to 2017, covering six different position views and additional information on image acquisition and patient demography. The reports were labeled with 174 different radiographic findings, 19 differential diagnoses and 104 anatomic locations organized as a hierarchical ta…
G.B. Pant Government Engineering College（G.B. Pant政府工程学院）
Coronavirus is a large virus family consisting of diverse viruses, some of which disseminate among mammals and others cause sickness among humans. COVID-19 is highly contagious and is rapidly spreading, rendering its early diagnosis of preeminent status. Researchers, medical specialists and organizations all over the globe have been working tirelessly to combat this virus and help in its containment. In this paper, a novel neural network called WisdomNet has been proposed, for the diagnosis of COVID-19 using chest X-rays. The WisdomNet uses the concept of Wisdom of Crowds as its founding idea.…
