We introduce an accurate lung segmentation model for chest radiographs based on deep convolutional neural networks. Our model is based on atrous convolutional layers to increase the field-of-view of filters efficiently. To improve segmentation performances further, we also propose a multi-stage training strategy, network-wise training, which the current stage network is fed with both input images and the outputs from pre-stage network. It is shown that this strategy has an ability to reduce falsely predicted labels and produce smooth boundaries of lung fields. We evaluate the proposed model on a common benchmark dataset, JSRT, and achieve the state-of-the-art segmentation performances with much fewer model parameters.
Recent studies have shown that lung cancer screening using annual low-dose computed tomography (CT) reduces lung cancer mortality by 20% compared to traditional chest radiography. Therefore, CT lung screening has started to be used widely all across the world. However, analyzing these images is a serious burden for radiologists. In this study, we propose a novel and simple framework that analyzes CT lung screenings using convolutional neural networks (CNNs) and reduces false positives. Our framework shows that even non-complex architectures are very powerful to classify 3D nodule data when com…
Universitat Rovira i Virgili（罗维拉-威尔吉利大学）；University of Cagliari（卡利亚里大学）
In this paper, we examine the strength of deep learning technique for diagnosing lung cancer on medical image analysis problem. Convolutional neural networks (CNNs) models become popular among the pattern recognition and computer vision research area because of their promising outcome on generating high-level image representations. We propose a new deep learning architecture for learning high-level image representation to achieve high classification accuracy with low variance in medical image binary classification tasks. We aim to learn discriminant compact features at beginning of our deep co…
University of Bern（伯尔尼大学）；Bern University Hospital “Inselspital”（伯尔尼大学医院“Inselspital”）
Early diagnosis of interstitial lung diseases is crucial for their treatment, but even experienced physicians find it difficult, as their clinical manifestations are similar. In order to assist with the diagnosis, computer-aided diagnosis (CAD) systems have been developed. These commonly rely on a fixed scale classifier that scans CT images, recognizes textural lung patterns and generates a map of pathologies. In a previous study, we proposed a method for classifying lung tissue patterns using a deep convolutional neural network (CNN), with an architecture designed for the specific problem. In…
Cancer is the uncontrollable cell division of abnormal cells inside the human body, which can spread to other body organs. It is one of the non-communicable diseases (NCDs) and NCDs accounts for 71% of total deaths worldwide whereas lung cancer is the second most diagnosed cancer after female breast cancer. Cancer survival rate of lung cancer is only 19%. There are various methods for the diagnosis of lung cancer, such as X-ray, CT scan, PET-CT scan, bronchoscopy and biopsy. However, to know the subtype of lung cancer based on the tissue type H and E staining is widely used, where the staining…
MSAD-Net: Multiscale and Spatial Attention-based Dense Network for Lung Cancer Classification
Computer Science and Engineering Department, NIIT University, Jaipur, India（尼特大学计算机科学与工程系，印度斋普尔）；CSE department, Christ (Deemed to be University), Bangalore, India（基督大学（认定为大学）计算机科学与工程系，印度班加罗尔）；Faculty of Computing, Engineering and the Built Environment, Birmingham City University, UK（伯明翰城市大学计算、工程与建筑环境学院，英国）
Cancer Hospital, Academy of Medical Sciences（中国医学科学院肿瘤医院）
This paper proposes a new convolutional neural network with multiscale processing for detecting ground-glass opacity (GGO) nodules in 3D computed tomography (CT) images, which is referred to as PiaNet for short. PiaNet consists of a feature-extraction module and a prediction module. The former module is constructed by introducing pyramid multiscale source connections into a contracting-expanding structure. The latter module includes a bounding-box regressor and a classifier that are employed to simultaneously recognize GGO nodules and estimate bounding boxes at multiple scales. To train the pr…
Sunnybrook Health Sciences Centre（森尼布鲁克健康科学中心）；University of Waterloo（滑铁卢大学）；University of Toronto（多伦多大学）
Lung cancer is the leading cause for cancer related deaths. As such, there is an urgent need for a streamlined process that can allow radiologists to provide diagnosis with greater efficiency and accuracy. A powerful tool to do this is radiomics: a high-dimension imaging feature set. In this study, we take the idea of radiomics one step further by introducing the concept of discovery radiomics for lung cancer prediction using CT imaging data. In this study, we realize these custom radiomic sequencers as deep convolutional sequencers using a deep convolutional neural network learning architectu…
Traditional survival models such as the Cox proportional hazards model are typically based on scalar or categorical clinical features. With the advent of increasingly large image datasets, it has become feasible to incorporate quantitative image features into survival prediction. So far, this kind of analysis is mostly based on radiomics features, i.e. a fixed set of features that is mathematically defined a priori. To capture highly abstract information, it is desirable to learn the feature extraction using convolutional neural networks. However, for tomographic medical images, model training…
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 …
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）
Harvard University（哈佛大学）；University of Central Florida（中佛罗里达大学）
Lung cancer is the leading cause of cancer related mortality by a significant margin. While new technologies, such as image segmentation, have been paramount to improved detection and earlier diagnoses, there are still significant challenges in treating the disease. In particular, despite an increased number of curative resections, many postoperative patients still develop recurrent lesions. Consequently, there is a significant need for prognostic tools that can more accurately predict a patient's risk for recurrence. In this paper, we explore the use of convolutional neural networks (CNNs) fo…
Lung nodule classification on CT scan patches using 3D convolutional neural networks
Institute for Applied System Analysis of National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”（乌克兰国立技术大学“伊戈尔·西科尔斯基基辅理工学院”应用系统分析研究所）
VRVis Center for Virtual Reality and Visualization（VRVis虚拟现实与可视化中心）
The success of deep convolutional neural networks on image classification and recognition tasks has led to new applications in very diversified contexts, including the field of medical imaging. In this paper we investigate and propose neural network architectures for automated multi-class segmentation of anatomical organs in chest radiographs, namely for lungs, clavicles and heart. We address several open challenges including model overfitting, reducing number of parameters and handling of severely imbalanced data in CXR by fusing recent concepts in convolutional networks and adapting them to …
Lung nodules suffer large variation in size and appearance in CT images. Nodules less than 10mm can easily lose information after down-sampling in convolutional neural networks, which results in low sensitivity. In this paper, a combination of 3D image and feature pyramid is exploited to integrate lower-level texture features with high-level semantic features, thus leading to a higher recall. However, 3D operations are time and memory consuming, which aggravates the situation with the explosive growth of medical images. To tackle this problem, we propose a general curriculum training strategy …
University of Groningen, University Medical Center Groningen（格罗宁根大学医学中心）；Tianjin Medical University Cancer Institute and Hospital（天津医科大学肿瘤医院）；University of Twente（特温特大学）
Accurate pulmonary nodule detection is a crucial step in lung cancer screening. Computer-aided detection (CAD) systems are not routinely used by radiologists for pulmonary nodule detection in clinical practice despite their potential benefits. Maximum intensity projection (MIP) images improve the detection of pulmonary nodules in radiological evaluation with computed tomography (CT) scans. Inspired by the clinical methodology of radiologists, we aim to explore the feasibility of applying MIP images to improve the effectiveness of automatic lung nodule detection using convolutional neural netwo…
National Institute of Advanced Industrial Science and Technology（产业技术综合研究所）；Yamaguchi University（山口大学）；University of Electro-Communications（电气通信大学）
Transfer learning is a machine learning technique designed to improve generalization performance by using pre-trained parameters obtained from other learning tasks. For image recognition tasks, many previous studies have reported that, when transfer learning is applied to deep neural networks, performance improves, despite having limited training data. This paper proposes a two-stage feature transfer learning method focusing on the recognition of textural medical images. During the proposed method, a model is successively trained with massive amounts of natural images, some textural images, an…
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.
Lung and Colon cancer are one of the leading causes of mortality and morbidity in adults. Histopathological diagnosis is one of the key components to discern cancer type. The aim of the present research is to propose a computer aided diagnosis system for diagnosing squamous cell carcinomas and adenocarcinomas of lung as well as adenocarcinomas of colon using convolutional neural networks by evaluating the digital pathology images for these cancers. Hereby, rendering artificial intelligence as useful technology in the near future. A total of 2500 digital images were acquired from LC25000 datase…
The Ohio State University（俄亥俄州立大学）；University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校）；Rensselaer Polytechnic Institute（伦斯勒理工学院）
Deep learning, as a promising new area of machine learning, has attracted a rapidly increasing attention in the field of medical imaging. Compared to the conventional machine learning methods, deep learning requires no hand-tuned feature extractor, and has shown a superior performance in many visual object recognition applications. In this study, we develop a deep convolutional neural network (CNN) and apply it to thoracic CT images for the classification of lung nodules. We present the CNN architecture and classification accuracy for the original images of lung nodules. In order to understand…
The annotated medical images are usually expensive to be collected. This paper proposes a deep learning method on small data to classify Common Imaging Signs of Lung diseases (CISL) in computed tomography (CT) images. We explore both the real data and the data generated by Generative Adversarial Network (GAN) to improve the reliability and the generalization of learning. First, we use GAN to generate a large number of CISLs from small annotated data, which are difficult to be distinguished from real counterparts. These generated samples are used to pre-train a Convolutional Neural Network (CNN…
Deep Learning-Based Fetal Lung Segmentation from Diffusion-weighted MRI Images and Lung Maturity Evaluation for Fetal Growth Restriction
School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK（生物医学工程与成像科学学院，伦敦国王学院）；Elizabeth Garrett Anderson Institute for Women’s Health, University College, London, UK（埃利扎贝塔·加德纳妇女健康研究所，大学学院，伦敦）；University College London Hospital NHS Foundation Trust, London, UK（伦敦大学学院医院国家健康服务信托基金）；Department of Medical Physics and Biomedical Engineering, University College London Hospitals, London, UK（医学物理与生物医学工程系，伦敦大学学院医院）；Department of Radiology, University College London Hospitals NHS Foundation Trust, London, United Kingdom（放射科，伦敦大学学院医院国家健康服务信托基金）
AI Research Group, Institute of Mathematics（人工智能研究组，数学研究所）；Eötvös Loránd University（罗兰大学）
Chest X-ray is the most common test among medical imaging modalities. It is applied for detection and differentiation of, among others, lung cancer, tuberculosis, and pneumonia, the last with importance due to the COVID-19 disease. Integrating computer-aided detection methods into the radiologist diagnostic pipeline, greatly reduces the doctors' workload, increasing reliability and quantitative analysis. Here we present a novel deep learning approach for lung segmentation, a basic, but arduous task in the diagnostic pipeline. Our method uses state-of-the-art fully convolutional neural networks…
The abundance of overlapping anatomical structures appearing in chest radiographs can reduce the performance of lung pathology detection by automated algorithms (CAD) as well as the human reader. In this paper, we present a deep learning based image processing technique for enhancing the contrast of soft lung structures in chest radiographs using Fully Convolutional Neural Networks (FCNN). Two 2D FCNN architectures were trained to accomplish the task: The first performs 2D lung segmentation which is used for normalization of the lung area. The second FCNN is trained to extract lung structures.…
College of Aerospace Science and Engineering, National University of Defense Technology（国防科技大学航空航天科学与工程学院）；Eindhoven University of Technology（埃因霍温理工大学）；ScreenPoint Medical（ScreenPoint Medical 公司）；Pingan Technology（平安科技）；Lunit Inc.（Lunit 公司）；Queen Mary University of London（伦敦玛丽女王大学）；Beihang University（北京航空航天大学）；Beijing Advanced Innovation Center for Biomedical Engineering（北京生物医学工程高精尖创新中心）
Accurate segmentation of lung cancer in pathology slides is a critical step in improving patient care. We proposed the ACDC@LungHP (Automatic Cancer Detection and Classification in Whole-slide Lung Histopathology) challenge for evaluating different computer-aided diagnosis (CADs) methods on the automatic diagnosis of lung cancer. The ACDC@LungHP 2019 focused on segmentation (pixel-wise detection) of cancer tissue in whole slide imaging (WSI), using an annotated dataset of 150 training images and 50 test images from 200 patients. This paper reviews this challenge and summarizes the top 10 submi…
Kamnoetvidya Science Academy（甘烹碧科学学院）；National Electronics and Computer Technology Center（泰国国家电子和计算机技术中心）；Vidyasirimedhi Institute of Science and Technology（瓦莱拉克科学技术研究院）
Since, cancer is curable when diagnosed at an early stage, lung cancer screening plays an important role in preventive care. Although both low dose computed tomography (LDCT) and computed tomography (CT) scans provide more medical information than normal chest x-rays, there is very limited access to these technologies in rural areas. Recently, there is a trend in using computer-aided diagnosis (CADx) to assist in screening and diagnosing of cancer from biomedical images. In this study, the 121-layer convolutional neural network also known as DenseNet-121 by G. Huang et. al., along with the tra…
School of Computing and Information Technology, University of Wollongong（卧龙岗大学计算与信息技术学院）
The Coronavirus Disease 2019 (COVID-19) is affecting increasingly large number of people worldwide, posing significant stress to the health care systems. Early and accurate diagnosis of COVID-19 is critical in screening of infected patients and breaking the person-to-person transmission. Chest X-ray (CXR) based computer-aided diagnosis of COVID-19 using deep learning becomes a promising solution to this end. However, the diverse and various radiographic features of COVID-19 make it challenging, especially when considering each CXR scan typically only generates one single image. Data scarcity i…
High resolution computed tomography (HRCT) is the most important imaging modality for interstitial lung diseases, where the radiologists are interested in identifying certain patterns, and their volumetric and regional distribution. The use of machine learning can assist the radiologists with both these tasks by performing semantic segmentation. In this paper, we propose an interactive annotation-tool for semantic segmentation that assists the radiologist in labeling CT scans. The annotation tool is evaluated by six radiologists and radiology residents classifying healthy lung and reticular pa…
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…
Classification based deep learning models for lung cancer and disease using medical images
Laboratory for Artificial Intelligence of Personalized Medicine, School of Artificial Intelligence, Guilin University of Electronic Technology（人工智能个性化医学实验室，人工智能学院，桂林电子科技大学）；The Laboratory for Imagery, Vision and Artificial Intelligence, Ecole de Technologie Superieure（影像、视觉与人工智能实验室，蒙特利尔理工学院）
We introduce a new computer aided detection and diagnosis system for lung cancer screening with low-dose CT scans that produces meaningful probability assessments. Our system is based entirely on 3D convolutional neural networks and achieves state-of-the-art performance for both lung nodule detection and malignancy classification tasks on the publicly available LUNA16 and Kaggle Data Science Bowl challenges. While nodule detection systems are typically designed and optimized on their own, we find that it is important to consider the coupling between detection and diagnosis components. Exploiti…
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…
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 …
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…
Arkansas State University（阿肯色州立大学）；University of Minnesota（明尼苏达大学）；The Chinese University of Hong Kong（香港中文大学）；Shanghai University of Finance and Economics（上海财经大学）；Washington University（华盛顿大学）；University of Arkansas for Medical Sciences（阿肯色大学医学科学校区）
Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer. It remains challenging for computational approaches to achieve performance comparable to experienced radiologists. Here we present NoduleX, a systematic approach to predict lung nodule malignancy from CT data, based on deep learning convolutional neural networks (CNN). For training and validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort. All nodules were identified and classified by four experienc…
Imperial College London（伦敦帝国理工学院）；Imperial College NHS Trust（帝国理工学院国民医疗服务信托）
Automated lobar segmentation allows regional evaluation of lung disease and is important for diagnosis and therapy planning. Advanced statistical workflows permitting such evaluation is a needed area within respiratory medicine; their adoption remains slow, with poor workflow accuracy. Diseased lung regions often produce high-density zones on CT images, limiting an algorithm's execution to specify damaged lobes due to oblique or lacking fissures. This impact motivated developing an improved machine learning method to segment lung lobes that utilises tracheobronchial tree information to enhance…
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…
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…
Validated respiratory drug deposition predictions from 2D and 3D medical images with statistical shape models and convolutional neural networks
Heriot-Watt University（赫瑞瓦特大学）；Royal Hospital for Children and Young People, NHS Lothian（NHS洛锡安区儿童与青少年皇家医院）；University Hospital Southampton NHS Foundation Trust（南安普顿大学医院NHS基金会信托）；Brunel University（布鲁内尔大学）；University of Edinburgh（爱丁堡大学）
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…
Sberbank AI Laboratory（俄罗斯联邦储蓄银行人工智能实验室）；New York University（纽约大学）
Analysis of chest CT scans can be used in detecting parts of lungs that are affected by infectious diseases such as COVID-19.Determining the volume of lungs affected by lesions is essential for formulating treatment recommendations and prioritizingpatients by severity of the disease. In this paper we adopted an approach based on using an ensemble of deep convolutionalneural networks for segmentation of slices of lung CT scans. Using our models we are able to segment the lesions, evaluatepatients dynamics, estimate relative volume of lungs affected by lesions and evaluate the lung damage stage.…
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…
Biomedical Imaging Research Institute, Cedars-Sinai Medical Center（西达赛奈医学中心生物医学成像研究所）；Cedars-Sinai Medical Center（西达赛奈医学中心）；Semmelweis University（塞梅尔维斯大学）；Azienda Ospedaliero-Universitaria di Parma（帕尔马大学医院）；Monash Health（莫纳什医疗中心）；Centro Cardiologico Monzino IRCCS, University of Milan（米兰大学蒙齐诺心脏中心IRCCS）；Showa University School of Medicine（昭和大学医学院）
Quantitative lung measures derived from computed tomography (CT) have been demonstrated to improve prognostication in coronavirus disease (COVID-19) patients, but are not part of the clinical routine since required manual segmentation of lung lesions is prohibitively time-consuming. We propose a new fully automated deep learning framework for rapid quantification and differentiation between lung lesions in COVID-19 pneumonia from both contrast and non-contrast CT images using convolutional Long Short-Term Memory (ConvLSTM) networks. Utilizing the expert annotations, model training was performe…
Detection of pulmonary pathologies using convolutional neural networks, Data Augmentation, ResNet50 and Vision Transformers
Universidad Abierta Interamericana（泛美开放大学）；Faculty of Information Technology（信息技术学院）；Center for Advanced Studies in Information Technology（信息技术高级研究中心）；Interamerican Open University（泛美开放大学）
School of Professional Studies（专业研究学院）；Northwestern University（西北大学）
Artificial intelligence (AI) is disrupting the medical field as advances in modern technology allow common household computers to learn anatomical and pathological features that distinguish between healthy and disease with the accuracy of highly specialized, trained physicians. Computer vision AI applications use medical imaging, such as lung chest X-Rays (LCXRs), to facilitate diagnoses by providing second-opinions in addition to a physician's or radiologist's interpretation. Considering the advent of the current Coronavirus disease (COVID-19) pandemic, LCXRs may provide rapid insights to ind…
University of Michigan（密歇根大学）；Imbio LLC（Imbio有限责任公司）
Early detection of lung cancer is critical for improvement of patient survival. To address the clinical need for efficacious treatments, genetically engineered mouse models (GEMM) have become integral in identifying and evaluating the molecular underpinnings of this complex disease that may be exploited as therapeutic targets. Assessment of GEMM tumor burden on histopathological sections performed by manual inspection is both time consuming and prone to subjective bias. Therefore, an interplay of needs and challenges exists for computer-aided diagnostic tools, for accurate and efficient analys…
Medical Artificial Intelligence for Early Detection of Lung Cancer: A Survey
College of Computer Science and Artificial Intelligence, Southwest Minzu University, Chengdu, 610225, China（计算机科学与人工智能学院，西南民族大学）；The Australian National University, Canberra ACT 2601, Australia（澳大利亚国立大学）；School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China（计算机科学与技术学校，北京交通大学）；Hunan university of technology, Zhuzhou, 410072, China（湖南理工大学）；University of Adelaide, Adelaide, South Australia 5005, Australia（阿德莱德大学）；La Trobe University, Melbourne, Victoria 3086, Australia（拉筹伯大学）
University of Porto（波尔图大学）；Medsensio AS（Medsensio公司）；UiT The Arctic University of Norway（挪威北极大学）
We applied deep learning to create an algorithm for breathing phase detection in lung sound recordings, and we compared the breathing phases detected by the algorithm and manually annotated by two experienced lung sound researchers. Our algorithm uses a convolutional neural network with spectrograms as the features, removing the need to specify features explicitly. We trained and evaluated the algorithm using three subsets that are larger than previously seen in the literature. We evaluated the performance of the method using two methods. First, discrete count of agreed breathing phases (using…
Gordon Center for Medical Imaging, Massachusetts General Hospital（麻省总医院戈登医学成像中心）；Harvard Medical School（哈佛医学院）；Beijing International Center for Mathematical Research, Peking University（北京大学北京国际数学研究中心）
Computer aided diagnostic (CAD) system is crucial for modern med-ical imaging. But almost all CAD systems operate on reconstructed images, which were optimized for radiologists. Computer vision can capture features that is subtle to human observers, so it is desirable to design a CAD system op-erating on the raw data. In this paper, we proposed a deep-neural-network-based detection system for lung nodule detection in computed tomography (CT). A primal-dual-type deep reconstruction network was applied first to convert the raw data to the image space, followed by a 3-dimensional convolutional ne…
Lung nodule detection is a class imbalanced problem because nodules are found with much lower frequency than non-nodules. In the class imbalanced problem, conventional classifiers tend to be overwhelmed by the majority class and ignore the minority class. We therefore propose cascaded convolutional neural networks to cope with the class imbalanced problem. In the proposed approach, cascaded convolutional neural networks that perform as selective classifiers filter out obvious non-nodules. Successively, a convolutional neural network trained with a balanced data set calculates nodule probabilit…
ARTORG Center for Biomedical Engineering Research, University of Bern（伯尔尼大学ARTORG生物医学工程研究中心）；Department of Emergency Medicine, Bern University Hospital “Inselspital”（伯尔尼大学医院“Inselspital”急诊医学系）；University Clinic for Pneumonology, Bern University Hospital “Inselspital”（伯尔尼大学医院“Inselspital”肺科大学诊所）；Department of Diagnostic, Interventional and Pediatric Radiology, Bern University Hospital “Inselspital”（伯尔尼大学医院“Inselspital”诊断、介入和儿科放射学系）；Bern University Hospital “Inselspital”（伯尔尼大学医院“Inselspital”）；University of Bern（伯尔尼大学）
Early and accurate diagnosis of interstitial lung diseases (ILDs) is crucial for making treatment decisions, but can be challenging even for experienced radiologists. The diagnostic procedure is based on the detection and recognition of the different ILD pathologies in thoracic CT scans, yet their manifestation often appears similar. In this study, we propose the use of a deep purely convolutional neural network for the semantic segmentation of ILD patterns, as the basic component of a computer aided diagnosis (CAD) system for ILDs. The proposed CNN, which consists of convolutional layers with…
Lung cancer is the leading cause of cancer-related deaths in the past several years. A major challenge in lung cancer screening is the detection of lung nodules from computed tomography (CT) scans. State-of-the-art approaches in automated lung nodule classification use deep convolutional neural networks (CNNs). However, these networks require a large number of training samples to generalize well. This paper investigates the use of capsule networks (CapsNets) as an alternative to CNNs. We show that CapsNets significantly outperforms CNNs when the number of training samples is small. To increase…
