Pulmonary Nodule Malignancy Classification Using its Temporal Evolution with Two-Stream 3D Convolutional Neural Networks
Eurecat, Centre Tecnòlogic de Catalunya（加泰罗尼亚技术中心Eurecat）；Universitat Pompeu Fabra（庞培法布拉大学）；Vall d’Hebron University Hospital（瓦尔德希伯伦大学医院）；ICREA（加泰罗尼亚研究与高级研究院）
Xavier Rafael-Palou, Anton Aubanell, Ilaria Bonavita, Mario Ceresa, Gemma Piella, Vicent Ribas, Miguel A. González Ballester
Nodule malignancy assessment is a complex, time-consuming and error-prone task. Current clinical practice requires measuring changes in size and density of the nodule at different time-points. State of the art solutions rely on 3D convolutional neural networks built on pulmonary nodules obtained from single CT scan per patient. In this work, we propose a two-stream 3D convolutional neural network that predicts malignancy by jointly analyzing two pulmonary nodule volumes from the same patient taken at different time-points. Best results achieve 77% of F1-score in test with an increment of 9% and 12% of F1-score with respect to the same network trained with images from a single time-point.
Thomas Jefferson University Hospital（托马斯·杰斐逊大学医院）；Georgia Institute of Technology（佐治亚理工学院）；Memorial Sloan Kettering Cancer Center（纪念斯隆·凯特琳癌症中心）
Spiculations/lobulations, sharp/curved spikes on the surface of lung nodules, are good predictors of lung cancer malignancy and hence, are routinely assessed and reported by radiologists as part of the standardized Lung-RADS clinical scoring criteria. Given the 3D geometry of the nodule and 2D slice-by-slice assessment by radiologists, manual spiculation/lobulation annotation is a tedious task and thus no public datasets exist to date for probing the importance of these clinically-reported features in the SOTA malignancy prediction algorithms. As part of this paper, we release a large-scale Cl…
Candidate generation, the first stage for most computer aided detection (CAD) systems, rapidly scans the entire image data for any possible abnormality locations, while the subsequent stages of the CAD system refine the candidates list to determine the most probable or significant of these candidates. The candidate generator creates a list of the locations and provides a size estimate for each candidate. A multiscale scale-normalized Laplacian of Gaussian (LoG) filtering method for detecting pulmonary nodules in whole-lung CT scans, presented in this paper, achieves a high sensitivity for both…
National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology（国家空天地海一体化大数据应用技术国家工程实验室）；School of Computer Science and Engineering, Northwestern Polytechnical University（西北工业大学计算机科学与工程学院）
Lung nodule malignancy prediction is an essential step in the early diagnosis of lung cancer. Besides the difficulties commonly discussed, the challenges of this task also come from the ambiguous labels provided by annotators, since deep learning models may learn, even amplify, the bias embedded in them. In this paper, we propose a multi-view "divide-and-rule" (MV-DAR) model to learn from both reliable and ambiguous annotations for lung nodule malignancy prediction. According to the consistency and reliability of their annotations, we divide nodules into three sets: a consistent and reliable s…
Eurecat Centre Tecnològic de Catalunya（加泰罗尼亚技术中心）；Universitat Pompeu Fabra（庞培法布拉大学）；Vall d'Hebron University Hospital（瓦尔德希布伦大学医院）；ICREA（加泰罗尼亚高级研究院）
We address the problem of supporting radiologists in the longitudinal management of lung cancer. Therefore, we proposed a deep learning pipeline, composed of four stages that completely automatized from the detection of nodules to the classification of cancer, through the detection of growth in the nodules. In addition, the pipeline integrated a novel approach for nodule growth detection, which relied on a recent hierarchical probabilistic U-Net adapted to report uncertainty estimates. Also, a second novel method was introduced for lung cancer nodule classification, integrating into a two stre…
College of Physics and Information Engineering, Fuzhou University（福州大学物理与信息工程学院）；Thoracic Department, Fujian Medical University Union Hospital（福建医科大学附属协和医院胸外科）
Background and Objective:Computer-aided diagnosis (CAD) systems promote diagnosis effectiveness and alleviate pressure of radiologists. A CAD system for lung cancer diagnosis includes nodule candidate detection and nodule malignancy evaluation. Recently, deep learning-based pulmonary nodule detection has reached satisfactory performance ready for clinical application. However, deep learning-based nodule malignancy evaluation depends on heuristic inference from low-dose computed tomography volume to malignant probability, which lacks clinical cognition. Methods:In this paper, we propose a joint…
Wuhan University（武汉大学）；Hubei Luojia Laboratory（湖北珞珈实验室）；The University of Electro-Communications（电气通信大学）；JD Explore Academy（京东探索研究院）；Dianei Technology（点内科技）；Shanghai Jiao Tong University（上海交通大学）；EPFL（洛桑联邦理工学院）
Lung cancer is the leading cause of cancer death worldwide. The best solution for lung cancer is to diagnose the pulmonary nodules in the early stage, which is usually accomplished with the aid of thoracic computed tomography (CT). As deep learning thrives, convolutional neural networks (CNNs) have been introduced into pulmonary nodule detection to help doctors in this labor-intensive task and demonstrated to be very effective. However, the current pulmonary nodule detection methods are usually domain-specific, and cannot satisfy the requirement of working in diverse real-world scenarios. To a…
The City College of New York（纽约城市学院）；UMass CICS（马萨诸塞大学阿默斯特分校信息与计算机科学学院）；Google AI（谷歌人工智能）；Memorial Sloan-Kettering Cancer Center（纪念斯隆-凯特琳癌症中心）
Accurate detection of pulmonary nodules with high sensitivity and specificity is essential for automatic lung cancer diagnosis from CT scans. Although many deep learning-based algorithms make great progress for improving the accuracy of nodule detection, the high false positive rate is still a challenging problem which limited the automatic diagnosis in routine clinical practice. In this paper, we propose a novel pulmonary nodule detection framework based on a 3D Feature Pyramid Network (3DFPN) to improve the sensitivity of nodule detection by employing multi-scale features to increase the res…
Eurecat, Centre Tecnològic de Catalunya（加泰罗尼亚技术中心 Eurecat）；Universitat Pompeu Fabra（庞培法布拉大学）；ICREA
The early identification of malignant pulmonary nodules is critical for better lung cancer prognosis and less invasive chemo or radio therapies. Nodule malignancy assessment done by radiologists is extremely useful for planning a preventive intervention but is, unfortunately, a complex, time-consuming and error-prone task. This explains the lack of large datasets containing radiologists malignancy characterization of nodules. In this article, we propose to assess nodule malignancy through 3D convolutional neural networks and to integrate it in an automated end-to-end existing pipeline of lung …
Convolutional neural networks (CNNs) have been demonstrated to be highly effective in the field of pulmonary nodule detection. However, existing CNN based pulmonary nodule detection methods lack the ability to capture long-range dependencies, which is vital for global information extraction. In computer vision tasks, non-local operations have been widely utilized, but the computational cost could be very high for 3D computed tomography (CT) images. To address this issue, we propose a long short slice-aware network (LSSANet) for the detection of pulmonary nodules. In particular, we develop a ne…
Application of Computer Deep Learning Model in Diagnosis of Pulmonary Nodules
University of California, Davis（加州大学戴维斯分校）；University of Washington（华盛顿大学）；Northern Arizona University（北亚利桑那大学）；Arizona State University（亚利桑那州立大学）；University of Pennsylvania（宾夕法尼亚大学）；University of California San Diego（加州大学圣迭戈分校）
Dalian University of Technology（大连理工大学）；Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province（辽宁省泛在网络与服务软件重点实验室）
More and more attention has been paid to the segmentation of pulmonary nodules. Among the current methods based on deep learning, 3D segmentation methods directly input 3D images, which takes up a lot of memory and brings huge computation. However, most of the 2D segmentation methods with less parameters and calculation have the problem of lacking spatial relations between slices, resulting in poor segmentation performance. In order to solve these problems, we propose an adjacent slice feature guided 2.5D network. In this paper, we design an adjacent slice feature fusion model to introduce inf…
Diagnosis and treatment of multiple pulmonary nodules are clinically important but challenging. Prior studies on nodule characterization use solitary-nodule approaches on multiple nodular patients, which ignores the relations between nodules. In this study, we propose a multiple instance learning (MIL) approach and empirically prove the benefit to learn the relations between multiple nodules. By treating the multiple nodules from a same patient as a whole, critical relational information between solitary-nodule voxels is extracted. To our knowledge, it is the first study to learn the relations…
Dali University（大理大学）；Liuzhou Vocational & Technical College（柳州职业技术学院）；The Second Xiangya Hospital of Central South University（中南大学湘雅二医院）；Hunan Key Laboratory of Early Diagnosis and Precise Treatment of Lung Cancer（湖南省肺癌早期诊断与精准治疗重点实验室）；Early-Stage Lung Cancer Center（早期肺癌中心）
Detection of pulmonary nodules by CT is used for screening lung cancer in early stages.omputer aided diagnosis (CAD) based on deep-learning method can identify the suspected areas of pulmonary nodules in CT images, thus improving the accuracy and efficiency of CT diagnosis. The accuracy and robustness of deep learning models. Method:In this paper, we explore (1) the data augmentation method based on the generation model and (2) the model structure improvement method based on the embedding mechanism. Two strategies have been introduced in this study: a new data augmentation method and a embeddi…
Eurecat Centre Tecnològic de Catalunya（加泰罗尼亚埃urecat技术中心）；Universitat Pompeu Fabra（庞培法布拉大学）；Vall d’Hebron University Hospital（瓦尔德希伯伦大学医院）；ICREA（加泰罗尼亚研究与高等研究院）
Lung cancer follow-up is a complex, error prone, and time consuming task for clinical radiologists. Several lung CT scan images taken at different time points of a given patient need to be individually inspected, looking for possible cancerogenous nodules. Radiologists mainly focus their attention in nodule size, density, and growth to assess the existence of malignancy. In this study, we present a novel method based on a 3D siamese neural network, for the re-identification of nodules in a pair of CT scans of the same patient without the need for image registration. The network was integrated …
The City College of New York（纽约城市学院）；UMass CICS（马萨诸塞大学信息与计算机科学学院）；Google AI（谷歌人工智能）；Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）
Accurate detection of pulmonary nodules with high sensitivity and specificity is essential for automatic lung cancer diagnosis from CT scans. Although many deep learning-based algorithms make great progress for improving the accuracy of nodule detection, the high false positive rate is still a challenging problem which limits the automatic diagnosis in routine clinical practice. Moreover, the CT scans collected from multiple manufacturers may affect the robustness of Computer-aided diagnosis (CAD) due to the differences in intensity scales and machine noises. In this paper, we propose a novel …
Parse and Recall: Towards Accurate Lung Nodule Malignancy Prediction like Radiologists
Hupan Lab（湖畔实验室）；DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）；The First Affiliated Hospital of College of Medicine, Zhejiang University（浙江大学医学院附属第一医院）；Guangdong Provincial People’s Hospital（广东省人民医院）
CPLOYO: A Pulmonary Nodule Detection Model with Multi-Scale Feature Fusion and Nonlinear Feature Learning
Tongji Hospital, School of Medicine, Tongji University（同济大学医学院附属同济医院）；Shanghai Pulmonology Hospital, School of Medicine, Tongji University（同济大学医学院附属上海肺科医院）；Carnegie Mellon University（卡内基梅隆大学）
MSDet: Receptive Field Enhanced Multiscale Detection for Tiny Pulmonary Nodule
College of Computer Science and Artificial Intelligence, Southwest Minzu University（西南民族大学计算机科学与人工智能学院）；School of Intelligent System Engineering, Sun Yat-sen University（中山大学智能系统工程学院）；University of Warwick（华威大学）；Australian National University（澳大利亚国立大学）；School of Computer Science and Technology, Beijing Jiaotong University（北京交通大学计算机科学与技术学院）；University of Toronto（多伦多大学）；University of Adelaide（阿德莱德大学）；La Trobe University（拉筹伯大学）
A Deep Learning Framework for Thyroid Nodule Segmentation and Malignancy Classification from Ultrasound Images
Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, UK（沃里克大学计算机科学系组织图像分析中心，英国）；Royal Stoke University Hospital, University Hospitals of North Midlands, UK（北中部大学医院皇家斯托医院，英国）
LMLCC-Net: A Semi-Supervised Deep Learning Model for Lung Nodule Malignancy Prediction from CT Scans using a Novel Hounsfield Unit-Based Intensity Filtering
mHealth Lab, Department of Biomedical Engineering, Bangladesh University of Engineering and Technology（孟加拉国工程与技术大学生物医学工程系mHealth实验室）
Median Technologies（Median科技公司）；Univ. Artois（阿图瓦大学）
Pathologies systematically induce morphological changes, thus providing a major but yet insufficiently quantified source of observables for diagnosis. The study develops a predictive model of the pathological states based on morphological features (3D-morphomics) on Computed Tomography (CT) volumes. A complete workflow for mesh extraction and simplification of an organ's surface is developed, and coupled with an automatic extraction of morphological features given by the distribution of mean curvature and mesh energy. An XGBoost supervised classifier is then trained and tested on the 3D-morpho…
Shanghai Jiao Tong University（上海交通大学）；Imperial College London（帝国理工学院）；Shanghai Chest Hospital（上海市胸科医院）；Shanghai Center for Brain Science and Brain-Inspired Technology（上海脑科学与类脑研究中心）
The LIDC-IDRI database is the most popular benchmark for lung cancer prediction. However, with subjective assessment from radiologists, nodules in LIDC may have entirely different malignancy annotations from the pathological ground truth, introducing label assignment errors and subsequent supervision bias during training. The LIDC database thus requires more objective labels for learning-based cancer prediction. Based on an extra small dataset containing 180 nodules diagnosed by pathological examination, we propose to re-label LIDC data to mitigate the effect of original annotation bias verifi…
Sir Run Run Shaw Hospital（邵逸夫医院）；School of Medicine, Zhejiang University（浙江大学医学院）；School of Computer Science and Technology, Hangzhou Dianzi University（杭州电子科技大学计算机科学与技术学院）；College of Medicine, Zhejiang University（浙江大学医学院）
Automatic pulmonary nodules classification is significant for early diagnosis of lung cancers. Recently, deep learning techniques have enabled remarkable progress in this field. However, these deep models are typically of high computational complexity and work in a black-box manner. To combat these challenges, in this work, we aim to build an efficient and (partially) explainable classification model. Specially, we use \emph{neural architecture search} (NAS) to automatically search 3D network architectures with excellent accuracy/speed trade-off. Besides, we use the convolutional block attenti…
The mortality of lung cancer has ranked high among cancers for many years. Early detection of lung cancer is critical for disease prevention, cure, and mortality rate reduction. However, existing detection methods on pulmonary nodules introduce an excessive number of false positive proposals in order to achieve high sensitivity, which is not practical in clinical situations. In this paper, we propose the multi-head detection and spatial squeeze-and-attention network, MHSnet, to detect pulmonary nodules, in order to aid doctors in the early diagnosis of lung cancers. Specifically, we first intr…
Shanghai Jiao Tong University（上海交通大学）；INSA Lyon（里昂国立应用科学学院）；CNRS（法国国家科学研究中心）；INSERM（法国国家健康与医学研究院）；Université de Lyon（里昂大学）；Guizhou University（贵州大学）；Shanghai Chest Hospital（上海市胸科医院）
To investigate whether the pleurae, airways and vessels surrounding a nodule on non-contrast computed tomography (CT) can discriminate benign and malignant pulmonary nodules. The LIDC-IDRI dataset, one of the largest publicly available CT database, was exploited for study. A total of 1556 nodules from 694 patients were involved in statistical analysis, where nodules with average scorings <3 and >3 were respectively denoted as benign and malignant. Besides, 339 nodules from 113 patients with diagnosis ground-truth were independently evaluated. Computer algorithms were developed to segment pulmo…
Lung cancer has the highest mortality rate of deadly cancers in the world. Early detection is essential to treatment of lung cancer. However, detection and accurate diagnosis of pulmonary nodules depend heavily on the experiences of radiologists and can be a heavy workload for them. Computer-aided diagnosis (CAD) systems have been developed to assist radiologists in nodule detection and diagnosis, greatly easing the workload while increasing diagnosis accuracy. Recent development of deep learning, greatly improved the performance of CAD systems. However, lack of model reliability and interpret…
Yandex School of Data Analysis（Yandex数据分析学院）；Tel Aviv University（特拉维夫大学）
Content-based retrieval supports a radiologist decision making process by presenting the doctor the most similar cases from the database containing both historical diagnosis and further disease development history. We present a deep learning system that transforms a 3D image of a pulmonary nodule from a CT scan into a low-dimensional embedding vector. We demonstrate that such a vector representation preserves semantic information about the nodule and offers a viable approach for content-based image retrieval (CBIR). We discuss the theoretical limitations of the available datasets and overcome …
University of Technology Sydney（悉尼科技大学）；IBM Australia Limited（IBM澳大利亚有限公司）；Sejong University（世宗大学）；Universiti Kebangsaan Malaysia（马来西亚国民大学）；Charles Sturt University（查尔斯特大学）
Lung cancer is the leading cause of cancer death and morbidity worldwide. Many studies have shown machine learning models to be effective at detecting lung nodules from chest X-ray images. However, these techniques have yet to be embraced by the medical community due to several practical, ethical, and regulatory constraints stemming from the black-box nature of deep learning models. Additionally, most lung nodules visible on chest X-ray are benign; therefore, the narrow task of computer vision-based lung nodule detection cannot be equated to automated lung cancer detection. Addressing both con…
Scale-aware Test-time Click Adaptation for Pulmonary Nodule and Mass Segmentation
Wuhan University（武汉大学）；Artificial Intelligence Institute of Wuhan University（武汉大学人工智能研究院）；Swiss Federal Institute of Technology Lausanne (EPFL)（洛桑联邦理工学院（EPFL））；Dianei Technology（点内科技）
University of Patras（帕特雷大学）；School of Medicine（医学院）；Laboratory of Nuclear Medicine（核医学实验室）
Lung Cancer is the most common cause of cancer-related death worldwide. Early and automatic diagnosis of Solitary Pulmonary Nodules (SPN) in Computer Tomography (CT) chest scans can provide early treatment as well as doctor liberation from time-consuming procedures. Deep Learning has been proven as a popular and influential method in many medical imaging diagnosis areas. In this study, we consider the problem of diagnostic classification between benign and malignant lung nodules in CT images derived from a PET/CT scanner. More specifically, we intend to develop experimental Convolutional Neura…
Ultrasound (US) is the primary imaging technique for the diagnosis of thyroid cancer. However, accurate identification of nodule malignancy is a challenging task that can elude less-experienced clinicians. Recently, many computer-aided diagnosis (CAD) systems have been proposed to assist this process. However, most of them do not provide the reasoning of their classification process, which may jeopardize their credibility in practical use. To overcome this, we propose a novel deep learning framework called multi-attribute attention network (MAA-Net) that is designed to mimic the clinical diagn…
University College London（伦敦大学学院）；MANAS AI（MANAS人工智能公司）；University College London Hospitals NHS Foundation Trust（伦敦大学学院医院国民保健服务基金会信托）；Royal Brompton and Harefield NHS Foundation Trust（皇家布朗普顿与哈菲尔德国民保健服务基金会信托）；Imperial College（帝国理工学院）
Lung cancer is the leading cause of cancer-related mortality worldwide. Lung cancer screening (LCS) using annual low-dose computed tomography (CT) scanning has been proven to significantly reduce lung cancer mortality by detecting cancerous lung nodules at an earlier stage. Improving risk stratification of malignancy risk in lung nodules can be enhanced using machine/deep learning algorithms. However most existing algorithms: a) have primarily assessed single time-point CT data alone thereby failing to utilize the inherent advantages contained within longitudinal imaging datasets; b) have not …
Reproducible Benchmarking for Lung Nodule Detection and Malignancy Classification Across Multiple Low-Dose CT Datasets
Duke University School of Medicine（杜克大学医学院）；Pratt School of Engineering, Duke University（杜克大学普拉特工程学院）；University of Colorado（科罗拉多大学）；Duke University（杜克大学）
University of Maryland Baltimore County（马里兰大学巴尔的摩县分校）
We present a hybrid algorithm to estimate lung nodule malignancy that combines imaging biomarkers from Radiologist's annotation with image classification of CT scans. Our algorithm employs a 3D Convolutional Neural Network (CNN) as well as a Random Forest in order to combine CT imagery with biomarker annotation and volumetric radiomic features. We analyze and compare the performance of the algorithm using only imagery, only biomarkers, combined imagery + biomarkers, combined imagery + volumetric radiomic features and finally the combination of imagery + biomarkers + volumetric features in orde…
Otto von Guericke University Magdeburg（马格德堡奥托·冯·格里克大学）
Pulmonary cancer is one of the most commonly diagnosed and fatal cancers and is often diagnosed by incidental findings on computed tomography. Automated pulmonary nodule detection is an essential part of computer-aided diagnosis, which is still facing great challenges and difficulties to quickly and accurately locate the exact nodules' positions. This paper proposes a dual skip connection upsampling strategy based on Dual Path network in a U-Net structure generating multiscale feature maps, which aims to minimize the ratio of false positives and maximize the sensitivity for lesion detection of…
Alleviating Hyperparameter-Tuning Burden in SVM Classifiers for Pulmonary Nodules Diagnosis with Multi-Task Bayesian Optimization
Yau Mathematical Sciences Center Tsinghua University（姚数学科学中心清华大学）；The First Affiliated Hospital of Guangzhou Medical University（广州医科大学第一附属医院）；China State Key Laboratory of Respiratory Disease（中国呼吸疾病国家重点实验室）；Academy of Mathematics and Systems Science Chinese Academy of Sciences（数学科学学院中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）；Department of Thoracic Surgery and Oncology The First Affiliated Hospital of Guangzhou Medical University（胸外科与肿瘤科广州医科大学第一附属医院）
Concordia University（康考迪亚大学）；Concordia Institute for Information Systems Engineering（康考迪亚信息系统工程研究所）；Sunnybrook Health Sciences Centre（桑尼布鲁克健康科学中心）；University of Toronto（多伦多大学）
The paper proposes a novel hybrid discovery Radiomics framework that simultaneously integrates temporal and spatial features extracted from non-thin chest Computed Tomography (CT) slices to predict Lung Adenocarcinoma (LUAC) malignancy with minimum expert involvement. Lung cancer is the leading cause of mortality from cancer worldwide and has various histologic types, among which LUAC has recently been the most prevalent. LUACs are classified as pre-invasive, minimally invasive, and invasive adenocarcinomas. Timely and accurate knowledge of the lung nodules malignancy leads to a proper treatme…
Longitudinal Multimodal Transformer Integrating Imaging and Latent Clinical Signatures From Routine EHRs for Pulmonary Nodule Classification
Vanderbilt University（范德堡大学）；Siemens Healthineers（西门子医疗）；Saint Luke’s Mid America Heart Institute（圣卢克中美洲心脏研究所）；Vanderbilt University Medical Center（范德堡大学医学中心）
Shanghai Jiao Tong University（上海交通大学）；Shanghai Chest Hospital（上海市胸科医院）；Imperial College London（帝国理工学院）
Recent evolution in deep learning has proven its value for CT-based lung nodule classification. Most current techniques are intrinsically black-box systems, suffering from two generalizability issues in clinical practice. First, benign-malignant discrimination is often assessed by human observers without pathologic diagnoses at the nodule level. We termed these data as "unsure data". Second, a classifier does not necessarily acquire reliable nodule features for stable learning and robust prediction with patch-level labels during learning. In this study, we construct a sure dataset with patholo…
Shanghai Jiao Tong University（上海交通大学）；Shanghai Institute for Advanced Communication and Data Science（上海先进通信与数据科学研究院）；AI Institute Shanghai Jiao Tong University（上海交通大学人工智能研究院）；Shanghai Chest Hospital（上海市胸科医院）；Shanghai General Hospital（上海市第一人民医院）
Follow-up serves an important role in the management of pulmonary nodules for lung cancer. Imaging diagnostic guidelines with expert consensus have been made to help radiologists make clinical decision for each patient. However, tumor growth is such a complicated process that it is difficult to stratify high-risk nodules from low-risk ones based on morphologic characteristics. On the other hand, recent deep learning studies using convolutional neural networks (CNNs) to predict the malignancy score of nodules, only provides clinicians with black-box predictions. To this end, we propose a unifie…
Objectives: To compare artificial intelligence (AI) as a second reader in detecting lung nodules on chest X-rays (CXR) versus radiologists of two binational institutions, and to evaluate AI performance when using two different modes: automated versus assisted (additional remote radiologist review). Methods: The CXR public database (n = 247) of the Japanese Society of Radiological Technology with various types and sizes of lung nodules was analyzed. Eight radiologists evaluated the CXR images with regard to the presence of lung nodules and nodule conspicuity. After radiologist review, the AI so…
Concordia Institute for Information Systems Engineering, Concordia University（康考迪亚大学信息系统工程研究所）；Sunnybrook Health Sciences Centre, University of Toronto（多伦多大学桑尼布鲁克健康科学中心）；McGill University Health Center- Research Institute（麦吉尔大学健康中心研究所）；Babak Imaging Center（巴布克影像中心）；Department of Electrical and Computer Engineering, University of Toronto（多伦多大学电气与计算机工程系）
Lung diseases including infections such as Pneumonia, Tuberculosis, and novel Coronavirus (COVID-19), together with Lung Cancer are significantly widespread and are, typically, considered life threatening. In particular, lung cancer is among the most common and deadliest cancers with a low 5-year survival rate. Timely diagnosis of lung cancer is, therefore, of paramount importance as it can save countless lives. In this regard, deep learning radiomics solutions have the promise of extracting the most useful features on their own in an end-to-end fashion without having access to the annotated b…
Harbin Engineering University（哈尔滨工程大学）；Monash University（莫纳什大学）；Beijing Airdoc Technology Co., Ltd.（北京鹰瞳科技发展股份有限公司）；Chongqing Hospital of Traditional Chinese Medicine（重庆市中医院）；Chongqing Renji Hospital of Chinese Academy of Sciences（中国科学院重庆仁济医院）
Usually, lesions are not isolated but are associated with the surrounding tissues. For example, the growth of a tumour can depend on or infiltrate into the surrounding tissues. Due to the pathological nature of the lesions, it is challenging to distinguish their boundaries in medical imaging. However, these uncertain regions may contain diagnostic information. Therefore, the simple binarization of lesions by traditional binary segmentation can result in the loss of diagnostic information. In this work, we introduce the image matting into the 3D scenes and use the alpha matte, i.e., a soft mask…
In recent years, the prevalence of several pulmonary diseases, especially the coronavirus disease 2019 (COVID-19) pandemic, has attracted worldwide attention. These diseases can be effectively diagnosed and treated with the help of lung imaging. With the development of deep learning technology and the emergence of many public medical image datasets, the diagnosis of lung diseases via medical imaging has been further improved. This article reviews pulmonary CT and X-ray image detection and classification in the last decade. It also provides an overview of the detection of lung nodules, pneumoni…
High-risk Factor Prediction in Lung Cancer Using Thin CT Scans: An Attention-Enhanced Graph Convolutional Network Approach
Renmin University of China（中国人民大学）；Capital Medical University（首都医科大学）；Beijing Chao-Yang Hospital（北京朝阳医院）；Beijing Institute of Respiratory Medicine（北京呼吸疾病研究所）
Jordan University of Science and Technology（约旦科技大学）
Purpose: The lung nodules localization in CT scan images is the most difficult task due to the complexity of the arbitrariness of shape, size, and texture of lung nodules. This is a challenge to be faced when coming to developing different solutions to improve detection systems. the deep learning approach showed promising results by using convolutional neural network (CNN), especially for image recognition and it's one of the most used algorithm in computer vision. Approach: we use (CNN) building blocks based on YOLOv5 (you only look once) to learn the features representations for nodule detec…
Suppression of thoracic bone shadows on chest X-rays (CXRs) has been indicated to improve the diagnosis of pulmonary disease. Previous approaches can be categorized as unsupervised physical and supervised deep learning models. Nevertheless, with physical models able to preserve morphological details but at the cost of extremely long processing time, existing DL methods face challenges of gathering sufficient/qualitative ground truth (GT) for robust training, thus leading to failure in maintaining clinically acceptable false positive rates. We hereby propose a generalizable yet efficient workfl…
Indian Institute of Technology Kharagpur（印度理工学院卡拉格普尔分校）；Intel Technology India Pvt. Ltd.（英特尔技术印度私人有限公司）
Lung cancer is the most common form of cancer found worldwide with a high mortality rate. Early detection of pulmonary nodules by screening with a low-dose computed tomography (CT) scan is crucial for its effective clinical management. Nodules which are symptomatic of malignancy occupy about 0.0125 - 0.025\% of volume in a CT scan of a patient. Manual screening of all slices is a tedious task and presents a high risk of human errors. To tackle this problem we propose a computationally efficient two stage framework. In the first stage, a convolutional neural network (CNN) trained adversarially …
