Pulmonary diseases can cause severe respiratory problems, leading to sudden death if not treated timely. Many researchers have utilized deep learning systems to diagnose pulmonary disorders using chest X-rays (CXRs). However, such systems require exhaustive training efforts on large-scale data to effectively diagnose chest abnormalities. Furthermore, procuring such large-scale data is often infeasible and impractical, especially for rare diseases. With the recent advances in incremental learning, researchers have periodically tuned deep neural networks to learn different classification tasks with few training examples. Although, such systems can resist catastrophic forgetting, they treat the knowledge representations independently of each other, and this limits their classification performance. Also, to the best of our knowledge, there is no incremental learning-driven image diagnostic framework that is specifically designed to screen pulmonary disorders from the CXRs. To address this, we present a novel framework that can learn to screen different chest abnormalities incrementally. In addition to this, the proposed framework is penalized through an incremental learning loss function that infers Bayesian theory to recognize structural and semantic inter-dependencies between incrementally learned knowledge representations to diagnose the pulmonary diseases effectively, regardless of the scanner specifications. We tested the proposed framework on five public CXR datasets containing different chest abnormalities, where it outperformed various state-of-the-art system through various metrics.
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…
Henan Provincial Chest Hospital（河南省胸科医院）；Harbin Institute of Technology（哈尔滨工业大学）；Huiying Medical Technology (Beijing) Co., Ltd.（慧影医疗科技（北京）有限公司）
Objective: We propose an end-to-end CNN-based locating model for pulmonary tuberculosis (TB) diagnosis in radiographs. This model makes full use of chest radiograph (X-ray) for its improved accessibility, reduced cost and high accuracy for TB disease. Methods: Several specialized improvements are proposed for detection task in medical field. A false positive (FP) restrictor head is introduced for FP reduction. Anchor-oriented network heads is proposed in the position regression section. An optimization of loss function is designed for hard example mining. Results: The experimental results show…
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…
VUNO Inc.（VUNO公司）；Imperial College London（帝国理工学院）；Asan Medical Center（峨山医疗中心）；University of Ulsan（蔚山大学）
Generating large quantities of quality labeled data in medical imaging is very time consuming and expensive. The performance of supervised algorithms for various tasks on imaging has improved drastically over the years, however the availability of data to train these algorithms have become one of the main bottlenecks for implementation. To address this, we propose a semi-supervised learning method where pseudo-negative labels from unlabeled data are used to further refine the performance of a pulmonary nodule detection network in chest radiographs. After training with the proposed network, the…
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…
King’s College London（伦敦国王学院）；Guy’s & St Thomas’ NHS Foundation Trust（盖伊和圣托马斯国民保健服务基金会信托）；Imperial College Healthcare NHS Trust（帝国理工学院医疗国民保健服务信托）
Machine learning approaches hold great potential for the automated detection of lung nodules in chest radiographs, but training the algorithms requires vary large amounts of manually annotated images, which are difficult to obtain. Weak labels indicating whether a radiograph is likely to contain pulmonary nodules are typically easier to obtain at scale by parsing historical free-text radiological reports associated to the radiographs. Using a repositotory of over 700,000 chest radiographs, in this study we demonstrate that promising nodule detection performance can be achieved using weak label…
Indian Institute of Technology Kharagpur（印度理工学院卡哈拉格普尔分校）；Apollo Gleneagles Hospital（阿波罗格伦伊格尔斯医院）；Intel Technology India Pvt. Ltd.（英特尔技术印度私人有限公司）
Chest radiographs are primarily employed for the screening of cardio, thoracic and pulmonary conditions. Machine learning based automated solutions are being developed to reduce the burden of routine screening on Radiologists, allowing them to focus on critical cases. While recent efforts demonstrate the use of ensemble of deep convolutional neural networks(CNN), they do not take disease comorbidity into consideration, thus lowering their screening performance. To address this issue, we propose a Graph Neural Network (GNN) based solution to obtain ensemble predictions which models the dependen…
Total lung volume is an important quantitative biomarker and is used for the assessment of restrictive lung diseases. In this study, we investigate the performance of several deep-learning approaches for automated measurement of total lung volume from chest radiographs. 7621 posteroanterior and lateral view chest radiographs (CXR) were collected from patients with chest CT available. Similarly, 928 CXR studies were chosen from patients with pulmonary function test (PFT) results. The reference total lung volume was calculated from lung segmentation on CT or PFT data, respectively. This dataset …
Convolutional neural networks (CNN) are now being widely used for classifying and detecting pulmonary abnormalities in chest radiographs. Two complementary generalization properties of CNNs, translation invariance and equivariance, are particularly useful in detecting manifested abnormalities associated with pulmonary disease, regardless of their spatial locations within the image. However, these properties also come with the loss of exact spatial information and global relative positions of abnormalities detected in local regions. Global relative positions of such abnormalities may help disti…
Bharti School of Telecommunications Technology & Management, Indian Institute of Technology Delhi（印度理工学院德里分校巴蒂电信技术与管理学院）；Teleradiology Solutions（远程放射学解决方案公司）；CSIR-Institute of Genomics and Integrative Biology（CSIR基因组学与整合生物学研究所）；Christian Medical College, Vellore（韦洛尔基督教医学院）
A chest radiograph, commonly called chest x-ray (CxR), plays a vital role in the diagnosis of various lung diseases, such as lung cancer, tuberculosis, pneumonia, and many more. Automated segmentation of the lungs is an important step to design a computer-aided diagnostic tool for examination of a CxR. Precise lung segmentation is considered extremely challenging because of variance in the shape of the lung caused by health issues, age, and gender. The proposed work investigates the use of an efficient deep convolutional neural network for accurate segmentation of lungs from CxR. We attempt an…
Stony Brook University（石溪大学）；Newark Beth Israel Medical Center（纽瓦克贝斯以色列医疗中心）；University Hospitals Cleveland Medical Center（克利夫兰大学医院医疗中心）
Well-labeled datasets of chest radiographs (CXRs) are difficult to acquire due to the high cost of annotation. Thus, it is desirable to learn a robust and transferable representation in an unsupervised manner to benefit tasks that lack labeled data. Unlike natural images, medical images have their own domain prior; e.g., we observe that many pulmonary diseases, such as the COVID-19, manifest as changes in the lung tissue texture rather than the anatomical structure. Therefore, we hypothesize that studying only the texture without the influence of structure variations would be advantageous for …
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…
Radboud University Medical Center（拉德堡德大学医学中心）；Community Health Solutions（社区健康解决方案公司）；Interactive Research and Development（互动研究与发展机构）；The Indus Health Network（印度河健康网络）；Thirona（Thirona公司）；Universal Delft Ltd.（代尔夫特环球有限公司）
There is a growing interest in the automated analysis of chest X-Ray (CXR) as a sensitive and inexpensive means of screening susceptible populations for pulmonary tuberculosis. In this work we evaluate the latest version of CAD4TB, a commercial software platform designed for this purpose. Version 6 of CAD4TB was released in 2018 and is here tested on a fully independent dataset of 5565 CXR images with GeneXpert (Xpert) sputum test results available (854 Xpert positive subjects). A subset of 500 subjects (50% Xpert positive) was reviewed and annotated by 5 expert observers independently to obta…
Indian Institute of Technology Kharagpur（印度理工学院卡拉格普尔分校）；Apollo Gleneagles Hospital（阿波罗格伦伊格尔斯医院）；Intel Technology India Pvt. Ltd.（英特尔技术印度私人有限公司）
Chest radiographs are primarily employed for the screening of pulmonary and cardio-/thoracic conditions. Being undertaken at primary healthcare centers, they require the presence of an on-premise reporting Radiologist, which is a challenge in low and middle income countries. This has inspired the development of machine learning based automation of the screening process. While recent efforts demonstrate a performance benchmark using an ensemble of deep convolutional neural networks (CNN), our systematic search over multiple standard CNN architectures identified single candidate CNN models whose…
University of California, Irvine（加利福尼亚大学尔湾分校）；Anivive Lifesciences, Inc.（Anivive生命科学公司）；University of Tennessee（田纳西大学）；The University of Arizona（亚利桑那大学）
Coccidioidomycosis is the most common systemic mycosis in dogs in the southwestern United States. With warming climates, affected areas and number of cases are expected to increase in the coming years, escalating also the chances of transmission to humans. As a result, developing methods for automating the detection of the disease is important, as this will help doctors and veterinarians more easily identify and diagnose positive cases. We apply machine learning models to provide accurate and interpretable predictions of Coccidioidomycosis. We assemble a set of radiographic images and use it t…
Radboud University Medical Center（拉德堡德大学医学中心）；Netherlands Cancer Institute (NKI)（荷兰癌症研究所）；Z 2 Sky Technologies Inc.（Z 2 Sky 技术公司）；Johns Hopkins University（约翰斯·霍普金斯大学）；University of Science and Technology of China（中国科学技术大学）；Institute of Computing Technology, CAS（中国科学院计算技术研究所）
Clinical evidence has shown that rib-suppressed chest X-rays (CXRs) can improve the reliability of pulmonary disease diagnosis. However, previous approaches on generating rib-suppressed CXR face challenges in preserving details and eliminating rib residues. We hereby propose a GAN-based disentanglement learning framework called Rib Suppression GAN, or RSGAN, to perform rib suppression by utilizing the anatomical knowledge embedded in unpaired computed tomography (CT) images. In this approach, we employ a residual map to characterize the intensity difference between CXR and the corresponding ri…
Deformable Image Registration of Dark-Field Chest Radiographs for Local Lung Signal Change Assessment
Technical University of Munich（慕尼黑工业大学）；Munich Institute for Biomedical Engineering, Technical University of Munich（慕尼黑工业大学慕尼黑生物医学工程研究所）；Klinikum rechts der Isar, Technical University of Munich（慕尼黑工业大学右岸医院）；TUM University Hospital（慕尼黑工业大学大学医院）；Philips Innovative Technologies（飞利浦创新技术公司）；Institute for Advanced Study, Technical University of Munich（慕尼黑工业大学高等研究院）
Qatar University（卡塔尔大学）；Bangladesh University of Engineering and Technology（孟加拉国工程技术大学）；Reem Medical Center（Reem医疗中心）；Hamad General Hospital（哈马德综合医院）；Weill Cornell Medicine - Qatar（威尔康奈尔医学院卡塔尔分校）
Coronavirus disease 2019 (COVID-19) has been the main agenda of the whole world, since it came into sight in December 2019 as it has significantly affected the world economy and healthcare system. Given the effects of COVID-19 on pulmonary tissues, chest radiographic imaging has become a necessity for screening and monitoring the disease. Numerous studies have proposed Deep Learning approaches for the automatic diagnosis of COVID-19. Although these methods achieved astonishing performance in detection, they have used limited chest X-ray (CXR) repositories for evaluation, usually with a few hun…
Seoul National University（首尔国立大学）；Hankuk University of Foreign Studies（韩国外国语大学）
Suppressing bones on chest X-rays such as ribs and clavicle is often expected to improve pathologies classification. These bones can interfere with a broad range of diagnostic tasks on pulmonary disease except for musculoskeletal system. Current conventional method for acquisition of bone suppressed X-rays is dual energy imaging, which captures two radiographs at a very short interval with different energy levels; however, the patient is exposed to radiation twice and the artifacts arise due to heartbeats between two shots. In this paper, we introduce a deep generative model trained to predict…
Lister Hill National Center for Biomedical Communications, National Library of Medicine（美国国家医学图书馆利斯特·希尔国家生物医学通信中心）；Takeda Pharmaceuticals（武田制药）；Saint Louis University（圣路易斯大学）；National Institutes of Health（美国国立卫生研究院）；Walt Whitman High School（沃尔特·惠特曼高中）
We demonstrate use of iteratively pruned deep learning model ensembles for detecting pulmonary manifestation of COVID-19 with chest X-rays. This disease is caused by the novel Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus, also known as the novel Coronavirus (2019-nCoV). A custom convolutional neural network and a selection of ImageNet pretrained models are trained and evaluated at patient-level on publicly available CXR collections to learn modality-specific feature representations. The learned knowledge is transferred and fine-tuned to improve performance and generalizat…
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…
X-ray2CTPA: Leveraging Diffusion Models to Enhance Pulmonary Embolism Classification
Faculty of Engineering, Tel Aviv University（工程学院，特拉维夫大学）；Division of Data-Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai（数据驱动与数字医学部，医学部，Mount Sinai医学院）；Department of Diagnostic Imaging, Sheba Medical Center, Ramat Gan, Israel（诊断影像科，Sheba 医疗中心，Ramat Gan，以色列）；Department of Radiology, Icahn School of Medicine, Mount Sinai（放射科，Icahn医学院，Mount Sinai）
Comparative Analysis of Deep Learning Architectures for Multi-Disease Classification of Single-Label Chest X-rays
Department of Computer Science, College of Science, Charmo University（查莫大学计算机科学系，科学学院）；Department of Software Engineering, University of Raparin（拉帕林大学软件工程系）
DeBoneDiT: Depth-Driven Conditional Bridge Diffusion Transformers for Bone Suppression in Chest X-Ray Images
Zhejiang University（浙江大学）；Hangzhou Dianzi University（杭州电子科技大学）；City University of Hong Kong（香港城市大学）；National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（国家癌症中心/国家临床医学研究中心/中国医学科学院北京协和医学院肿瘤医院深圳医院）；Shenzhen Research Institute of Big Data（深圳大数据研究院）
Automated diagnosis of lung diseases using vision transformer: a comparative study on chest x-ray classification
Centro de Investigación en Computación, Instituto Politécnico Nacional (CIC-IPN)（墨西哥国立理工学院计算机研究中心）；Grand Asian University of Sialkot（锡亚尔科特大亚洲大学）；The Islamia University of Bahawalpur（巴哈瓦尔布尔伊斯兰大学）；Zhejiang Normal University（浙江师范大学）；Leads University Lahore（拉合尔利兹大学）
Lung X-ray images, if processed using statistical and computational methods, can distinguish pneumonia from COVID-19. The present work shows that it is possible to extract lung X-ray characteristics to improve the methods of examining and diagnosing patients with suspected COVID-19, distinguishing them from malaria, dengue, H1N1, tuberculosis, and Streptococcus pneumonia. More precisely, an intelligent computational model was developed to process lung X-ray images and classify whether the image is of a patient with COVID-19. The images were processed and extracted their characteristics. These …
Disentanglement of Biological and Technical Factors via Latent Space Rotation in Clinical Imaging Improves Disease Pattern Discovery
Computational Imaging Research Lab, Department for Biomedical Imaging and Image-guided Therapy（生物医学成像与影像引导治疗部门计算成像研究实验室）；Comprehensive Center for Artificial Intelligence in Medicine（医学人工智能综合中心）；Division of General and Pediatric Radiology, Department for Biomedical Imaging and Image-guided Therapy（普通放射科与儿科放射科，生物医学成像与影像引导治疗部门）；Medical University of Vienna（维也纳医学大学）
Evaluating the Impact of Radiographic Noise on Chest X-ray Semantic Segmentation and Disease Classification Using a Scalable Noise Injection Framework
The University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心）；Stanford University School of Medicine（斯坦福大学医学院）；Algoverse AI Research（Algoverse AI 研究院）
