相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。Value of a deep learning-based algorithm for detecting Lung-RADS category 4 nodules on chest radiographs in a health checkup population: estimation of the sample size for a randomized controlled trial
Ju Gang Nam et al.
EUROPEAN RADIOLOGY (2022)
Application of Artificial Intelligence in Lung Cancer
Hwa-Yen Chiu et al.
CANCERS (2022)
Artificial Intelligence in Lung Cancer: Bridging the Gap Between Computational Power and Clinical Decision-Making
Jaryd R. Christie et al.
CANADIAN ASSOCIATION OF RADIOLOGISTS JOURNAL-JOURNAL DE L ASSOCIATION CANADIENNE DES RADIOLOGISTES (2021)
Multi-view Convolutional Recurrent Neural Networks for Lung Cancer Nodule Identification
Mian Muhammad Naeem Abid et al.
NEUROCOMPUTING (2021)
Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries
Hyuna Sung et al.
CA-A CANCER JOURNAL FOR CLINICIANS (2021)
Development and validation of a clinically applicable deep learning strategy (HONORS) for pulmonary nodule classification at CT: A retrospective multicentre study
Wenhui Lv et al.
LUNG CANCER (2021)
AI applications to medical images: From machine learning to deep learning
Isabella Castiglioni et al.
PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS (2021)
Radiomics and artificial intelligence in lung cancer screening
Franciszek Binczyk et al.
TRANSLATIONAL LUNG CANCER RESEARCH (2021)
Basic of machine learning and deep learning in imaging for medical physicists
Luigi Manco et al.
PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS (2021)
Deep Learning for Malignancy Risk Estimation of Pulmonary Nodules Detected at Low-Dose Screening CT
Kiran Vaidhya Venkadesh et al.
RADIOLOGY (2021)
An Integrated Deep Learning Algorithm for Detecting Lung Nodules With Low-Dose CT and Its Application in 6G-Enabled Internet of Medical Things
Wei Wang et al.
IEEE INTERNET OF THINGS JOURNAL (2021)
The current issues and future perspective of artificial intelligence for developing new treatment strategy in non-small cell lung cancer: harmonization of molecular cancer biology and artificial intelligence
Ichidai Tanaka et al.
CANCER CELL INTERNATIONAL (2021)
Artificial intelligence for detection and characterization of pulmonary nodules in lung cancer CT screening: ready for practice?
Anton Schreuder et al.
TRANSLATIONAL LUNG CANCER RESEARCH (2021)
The radiologist's role in lung cancer screening
Annemiek Snoeckx et al.
TRANSLATIONAL LUNG CANCER RESEARCH (2021)
Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis
Ravi Aggarwal et al.
NPJ DIGITAL MEDICINE (2021)
Artificial intelligence applications for thoracic imaging
Guillaume Chassagnon et al.
EUROPEAN JOURNAL OF RADIOLOGY (2020)
Deep learning: definition and perspectives for thoracic imaging
Guillaume Chassagnon et al.
EUROPEAN RADIOLOGY (2020)
GRADE guidelines: 21 part 1. Study design, risk of bias, and indirectness in rating the certainty across a body of evidence for test accuracy
Holger J. Schunemann et al.
JOURNAL OF CLINICAL EPIDEMIOLOGY (2020)
GRADE guidelines: 21 part 2. Test accuracy: inconsistency, imprecision, publication bias, and other domains for rating the certainty of evidence and presenting it in evidence profiles and summary of findings tables
Holger J. Schunemann et al.
JOURNAL OF CLINICAL EPIDEMIOLOGY (2020)
Novel convolutional neural network architecture for improved pulmonary nodule classification on computed tomography
Yi Wang et al.
MULTIDIMENSIONAL SYSTEMS AND SIGNAL PROCESSING (2020)
Identification of benign and malignant pulmonary nodules on chest CT using improved 3D U-Net deep learning framework
Kaiqiang Yang et al.
EUROPEAN JOURNAL OF RADIOLOGY (2020)
Artificial intelligence and radiomics enhance the positive predictive value of digital chest tomosynthesis for lung cancer detection within SOS clinical trial
Stephane Chauvie et al.
EUROPEAN RADIOLOGY (2020)
Differentiation of Benign from Malignant Pulmonary Nodules by Using a Convolutional Neural Network to Determine Volume Change at Chest CT
Yoshiharu Ohno et al.
RADIOLOGY (2020)
The Use of Artificial Intelligence in the Differentiation of Malignant and Benign Lung Nodules on Computed Tomograms Proven by Surgical Pathology
Yung-Liang Wan et al.
CANCERS (2020)
Artificial neural networks improve LDCT lung cancer screening: a comparative validation study
Yin-Chen Hsu et al.
BMC CANCER (2020)
Developing specific reporting guidelines for diagnostic accuracy studies assessing AI interventions: The STARD-AI Steering Group
Viknesh Sounderajah et al.
NATURE MEDICINE (2020)
MSCS-DeepLN: Evaluating lung nodule malignancy using multi-scale cost-sensitive neural networks
Xiuyuan Xu et al.
MEDICAL IMAGE ANALYSIS (2020)
A primer for understanding radiology articles about machine learning and deep learning
Takeshi Nakaura et al.
DIAGNOSTIC AND INTERVENTIONAL IMAGING (2020)
Deep learning delivers early detection
Elizabeth Svoboda
NATURE (2020)
Artificial Intelligence Tools for Refining Lung Cancer Screening
J. Luis Espinoza et al.
JOURNAL OF CLINICAL MEDICINE (2020)
2D CNN versus 3D CNN for false-positive reduction in lung cancer screening
Juezhao Yu et al.
JOURNAL OF MEDICAL IMAGING (2020)
Role of imaging biomarkers in mutation-driven non-small cell lung cancer
Dexter P. Mendoza et al.
WORLD JOURNAL OF CLINICAL ONCOLOGY (2020)
How to: evaluate a diagnostic test
M. M. G. Leeflang et al.
CLINICAL MICROBIOLOGY AND INFECTION (2019)
Radiological images and machine learning: Trends, perspectives, and prospects
Zhenwei Zhang et al.
COMPUTERS IN BIOLOGY AND MEDICINE (2019)
Lung Cancer Screening, towards a Multidimensional Approach: Why and How?
Jonathan Benzaquen et al.
CANCERS (2019)
Development of an interactive web-based tool to conduct and interrogate meta-analysis of diagnostic test accuracy studies: MetaDTA
Suzanne C. Freeman et al.
BMC MEDICAL RESEARCH METHODOLOGY (2019)
End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography
Diego Ardila et al.
NATURE MEDICINE (2019)
Toward an Expert Level of Lung Cancer Detection and Classification Using a Deep Convolutional Neural Network
Chao Zhang et al.
ONCOLOGIST (2019)
Non-Small Cell Lung Cancer: Epidemiology, Screening, Diagnosis, and Treatment
Narjust Duma et al.
MAYO CLINIC PROCEEDINGS (2019)
Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI
Maciej A. Mazurowski et al.
JOURNAL OF MAGNETIC RESONANCE IMAGING (2019)
A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis
Xiaoxuan Liu et al.
LANCET DIGITAL HEALTH (2019)
A novel approach for detection of Lung Cancer using Digital Image Processing and Convolution Neural Networks
Rohit Y. Bhalerao et al.
2019 5TH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTING & COMMUNICATION SYSTEMS (ICACCS) (2019)
Effective and Reliable Framework for Lung Nodules Detection from CT Scan Images
Sajid Ali Khan et al.
SCIENTIFIC REPORTS (2019)
Using Sequential Decision Making to Improve Lung Cancer Screening Performance
Panayiotis Petousis et al.
IEEE ACCESS (2019)
The utilisation of convolutional neural networks in detecting pulmonary nodules: a review
Andrew Murphy et al.
BRITISH JOURNAL OF RADIOLOGY (2018)
Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies The PRISMA-DTA Statement
Matthew D. F. McInnes et al.
JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION (2018)
Added Value of Computer-aided CT Image Features for Early Lung Cancer Diagnosis with Small Pulmonary Nodules: A Matched Case-Control Study
Peng Huang et al.
RADIOLOGY (2018)
A Generalized Deep Learning-Based Diagnostic System for Early Diagnosis of Various Types of Pulmonary Nodules
Ahmed Shaffie et al.
TECHNOLOGY IN CANCER RESEARCH & TREATMENT (2018)
A Deep Learning Method for Early Screening of Lung Cancer
Kunpeng Zhang et al.
NINTH INTERNATIONAL CONFERENCE ON GRAPHIC AND IMAGE PROCESSING (ICGIP 2017) (2018)
Artificial intelligence in radiology
Ahmed Hosny et al.
NATURE REVIEWS CANCER (2018)
Convolutional neural networks: an overview and application in radiology
Rikiya Yamashita et al.
INSIGHTS INTO IMAGING (2018)
Overview of the Process of Conducting Meta-analyses of the Diagnostic Test Accuracy
Young Ho Lee
JOURNAL OF RHEUMATIC DISEASES (2018)
Lung cancer-A global perspective
Amanda McIntyre et al.
JOURNAL OF SURGICAL ONCOLOGY (2017)
Lung cancer: current therapies and new targeted treatments
Fred R. Hirsch et al.
LANCET (2017)
A survey on deep learning in medical image analysis
Geert Litjens et al.
MEDICAL IMAGE ANALYSIS (2017)
Tracking the Evolution of Non-Small-Cell Lung Cancer
M. Jamal-Hanjani et al.
NEW ENGLAND JOURNAL OF MEDICINE (2017)
Machine Learning for Medical Imaging1
Bradley J. Erickson et al.
RADIOGRAPHICS (2017)
Screening and Biosensor-Based Approaches for Lung Cancer Detection
Lulu Wang
SENSORS (2017)
Progress and prospects of early detection in lung cancer
Sean Blandin Knight et al.
OPEN BIOLOGY (2017)
SCREENING AND EARLY DETECTION OF LUNG CANCER
Julia A. Eggert et al.
SEMINARS IN ONCOLOGY NURSING (2017)
Emotional Problems, Quality of Life, and Symptom Burden in Patients With Lung Cancer
Eleshia J. Morrison et al.
CLINICAL LUNG CANCER (2017)
A review of lung cancer screening and the role of computer-aided detection
B. Al Mohammad et al.
CLINICAL RADIOLOGY (2017)
STARD 2015: An Updated List of Essential Items for Reporting Diagnostic Accuracy Studies
Patrick M. Bossuyt et al.
CLINICAL CHEMISTRY (2015)
Systematic Review and Meta-Analysis of Studies Evaluating Diagnostic Test Accuracy: A Practical Review for Clinical. Researchers-Part II. Statistical. Methods of Meta-Analysis
Juneyoung Lee et al.
KOREAN JOURNAL OF RADIOLOGY (2015)
Diagnostic test accuracy: methods for systematic review and meta-analysis
Jared M. Campbell et al.
INTERNATIONAL JOURNAL OF EVIDENCE-BASED HEALTHCARE (2015)
Computer-aided detection system for lung cancer in computed tomography scans: Review and future prospects
Macedo Firmino et al.
BIOMEDICAL ENGINEERING ONLINE (2014)
Systematic reviews and meta-analyses of diagnostic test accuracy
M. M. G. Leeflang
CLINICAL MICROBIOLOGY AND INFECTION (2014)
Understanding Sources of Bias in Diagnostic Accuracy Studies
Robert L. Schmidt et al.
ARCHIVES OF PATHOLOGY & LABORATORY MEDICINE (2013)
Interrater reliability: the kappa statistic
Mary L. McHugh
BIOCHEMIA MEDICA (2012)
QUADAS-2: A Revised Tool for the Quality Assessment of Diagnostic Accuracy Studies
Penny F. Whiting et al.
ANNALS OF INTERNAL MEDICINE (2011)
Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening
Denise R. Aberle et al.
NEW ENGLAND JOURNAL OF MEDICINE (2011)
East meets West: ethnic differences in epidemiology and clinical behaviors of lung cancer between East Asians and Caucasians
Wei Zhou et al.
Chinese Journal of Cancer (2011)
Simple Statistical Measures for Diagnostic Accuracy Assessment
Jayawant N. Mandrekar
JOURNAL OF THORACIC ONCOLOGY (2010)
A hierarchical regression approach to meta-analysis of diagnostic test accuracy evaluations
CM Rutter et al.
STATISTICS IN MEDICINE (2001)
分享论文
