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。Deep Feature Learning for Medical Image Analysis with Convolutional Autoencoder Neural Network
Min Chen et al.
IEEE TRANSACTIONS ON BIG DATA (2021)
Development and Validation of Deep Learning-based Automatic Detection Algorithm for Malignant Pulmonary Nodules on Chest Radiographs
Ju Gang Nam et al.
RADIOLOGY (2019)
The practical implementation of artificial intelligence technologies in medicine
Jianxing He et al.
NATURE MEDICINE (2019)
Cancer Statistics, 2018
Rebecca L. Siegel et al.
CA-A CANCER JOURNAL FOR CLINICIANS (2018)
Hybrid-feature-guided lung nodule type classification on CT images
Jingjing Yuan et al.
COMPUTERS & GRAPHICS-UK (2018)
MC2ESVM: Multiclass Classification Based on Cooperative Evolution of Support Vector Machines
Alejandro Rosales-Perez et al.
IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE (2018)
Fusing texture, shape and deep model-learned information at decision level for automated classification of lung nodules on chest CT
Xie Yutong et al.
INFORMATION FUSION (2018)
Opportunities and obstacles for deep learning in biology and medicine
Travers Ching et al.
JOURNAL OF THE ROYAL SOCIETY INTERFACE (2018)
Reinventing Radiology: Big Data and the Future of Medical Imaging
Michael A. Morris et al.
JOURNAL OF THORACIC IMAGING (2018)
Long-Term Active Surveillance of Screening Detected Subsolid Nodules is a Safe Strategy to Reduce Overtreatment
Mario Silva et al.
JOURNAL OF THORACIC ONCOLOGY (2018)
Automatic detection of multisize pulmonary nodules in CT images: Large-scale validation of the false-positive reduction step
Anindya Gupta et al.
MEDICAL PHYSICS (2018)
Multi-view multi-scale CNNs for lung nodule type classification from CT images
Xinglong Liu et al.
PATTERN RECOGNITION (2018)
Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization
Mizuho Nishio et al.
PLOS ONE (2018)
An Automatic Detection System of Lung Nodule Based on Multigroup Patch-Based Deep Learning Network
Hongyang Jiang et al.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2018)
Deep learning aided decision support for pulmonary nodules diagnosing: a review
Yixin Yang et al.
JOURNAL OF THORACIC DISEASE (2018)
Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries
Freddie Bray et al.
CA-A CANCER JOURNAL FOR CLINICIANS (2018)
Deep Learning-A Technology With the Potential to Transform Health Care
Geoffrey Hinton
JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION (2018)
On the Prospects for a (Deep) Learning Health Care System
C. David Naylor
JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION (2018)
Clinical Implications and Challenges of Artificial Intelligence and Deep Learning
William W. Stead
JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION (2018)
GLOBOCAN 2018: counting the toll of cancer
[Anonymous]
LANCET (2018)
Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks
Andrea Soltoggio et al.
NEURAL NETWORKS (2018)
Computer-aided diagnosis of lung nodule classification between benign nodule, primary lung cancer, and metastatic lung cancer at different image size using deep convolutional neural network with transfer learning
Mizuho Nishio et al.
PLOS ONE (2018)
Automatic inference model construction for computer-aided diagnosis of lung nodule: Explanation adequacy, inference accuracy, and expert's knowledge
Masami Kawagishi et al.
PLOS ONE (2018)
Deep learning for healthcare: review, opportunities and challenges
Riccardo Miotto et al.
BRIEFINGS IN BIOINFORMATICS (2018)
Multilevel Contextual 3-D CNNs for False Positive Reduction in Pulmonary Nodule Detection
Qi Dou et al.
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING (2017)
Retrieval From and Understanding of Large-Scale Multi-modal Medical Datasets: A Review
Henning Mueller et al.
IEEE TRANSACTIONS ON MULTIMEDIA (2017)
Management of Progressive Pulmonary Nodules Found during and outside of CT Lung Cancer Screening Studies
Mathias Meyer et al.
JOURNAL OF THORACIC ONCOLOGY (2017)
European position statement on lung cancer screening
Matthijs Oudkerk et al.
LANCET ONCOLOGY (2017)
Relationship between nodule count and lung cancer probability in baseline CT lung cancer screening: The NELSON study
Marjolein A. Heuvelmans et al.
LUNG CANCER (2017)
A survey on deep learning in medical image analysis
Geert Litjens et al.
MEDICAL IMAGE ANALYSIS (2017)
Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge
Arnaud Arindra Adiyoso Setio et al.
MEDICAL IMAGE ANALYSIS (2017)
Dermatologist-level classification of skin cancer with deep neural networks
Andre Esteva et al.
NATURE (2017)
Multi-crop Convolutional Neural Networks for lung nodule malignancy suspiciousness classification
Wei Shen et al.
PATTERN RECOGNITION (2017)
Comparing two classes of end-to-end machine-learning models in lung nodule detection and classification: MTANNs vs. CNNs
Nima Tajbakhsh et al.
PATTERN RECOGNITION (2017)
Deep Learning: A Primer for Radiologists
Gabriel Chartrand et al.
RADIOGRAPHICS (2017)
Automatic Categorization and Scoring of Solid, Part-Solid and Non-Solid Pulmonary Nodules in CT Images with Convolutional Neural Network
Xiaoguang Tu et al.
SCIENTIFIC REPORTS (2017)
Deep Learning for Health Informatics
Daniele Ravi et al.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2017)
Towards automatic pulmonary nodule management in lung cancer screening with deep learning
Francesco Ciompi et al.
SCIENTIFIC REPORTS (2017)
Automatic feature learning using multichannel ROI based on deep structured algorithms for computerized lung cancer diagnosis
Wenqing Sun et al.
COMPUTERS IN BIOLOGY AND MEDICINE (2017)
Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning
Bram van Ginneken
RADIOLOGICAL PHYSICS AND TECHNOLOGY (2017)
Opportunities for Patient-centered Outcomes Research in Radiology
Matthew E. Zygmont et al.
ACADEMIC RADIOLOGY (2016)
Cancer Statistics in China, 2015
Wanqing Chen et al.
CA-A CANCER JOURNAL FOR CLINICIANS (2016)
Big Data and machine learning in radiation oncology: State of the art and future prospects
Jean-Emmanuel Bibault et al.
CANCER LETTERS (2016)
Automatic 3D pulmonary nodule detection in CT images: A survey
Igor Rafael S. Valente et al.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2016)
Low-dose computed tomography for lung cancer screening: comparison of performance between annual and biennial screen
Nicola Sverzellati et al.
EUROPEAN RADIOLOGY (2016)
Combining Generative and Discriminative Representation Learning for Lung CT Analysis With Convolutional Restricted Boltzmann Machines
Gijs van Tulder et al.
IEEE TRANSACTIONS ON MEDICAL IMAGING (2016)
Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique
Hayit Greenspan et al.
IEEE TRANSACTIONS ON MEDICAL IMAGING (2016)
Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?
Nima Tajbakhsh et al.
IEEE TRANSACTIONS ON MEDICAL IMAGING (2016)
Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network
Marios Anthimopoulos et al.
IEEE TRANSACTIONS ON MEDICAL IMAGING (2016)
Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks
Arnaud Arindra Adiyoso Setio et al.
IEEE TRANSACTIONS ON MEDICAL IMAGING (2016)
AggNet: Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images
Shadi Albarqouni et al.
IEEE TRANSACTIONS ON MEDICAL IMAGING (2016)
Fast Convolutional Neural Network Training Using Selective Data Sampling: Application to Hemorrhage Detection in Color Fundus Images
Mark J. J. P. van Grinsven et al.
IEEE TRANSACTIONS ON MEDICAL IMAGING (2016)
Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs
Varun Gulshan et al.
JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION (2016)
Four challenges in medical image analysis from an industrial perspective
Jurgen Weese et al.
MEDICAL IMAGE ANALYSIS (2016)
Automated detection of pulmonary nodules in PET/CT images: Ensemble false-positive reduction using a convolutional neural network technique
Atsushi Teramoto et al.
MEDICAL PHYSICS (2016)
LUNGx Challenge for computerized lung nodule classification
Samuel G. Armato et al.
JOURNAL OF MEDICAL IMAGING (2016)
Crowdsourcing healthcare costs: Opportunities and challenges for patient centered price transparency
Zachary F. Meisel et al.
HEALTHCARE-THE JOURNAL OF DELIVERY SCIENCE AND INNOVATION (2016)
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky et al.
INTERNATIONAL JOURNAL OF COMPUTER VISION (2015)
Large scale validation of the M5L lung CAD on heterogeneous CT datasets
E. Lopez Torres et al.
MEDICAL PHYSICS (2015)
Automatic detection of large pulmonary solid nodules in thoracic CT images
Arnaud A. A. Setio et al.
MEDICAL PHYSICS (2015)
Deep learning in neural networks: An overview
Juergen Schmidhuber
NEURAL NETWORKS (2015)
Measuring ruggedness in fitness landscapes
Jeremy Van Cleve et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2015)
Computer-aided classification of lung nodules on computed tomography images via deep learning technique
Kai-Lung Hua et al.
ONCOTARGETS AND THERAPY (2015)
Lung cancer probability in patients with CT-detected pulmonary nodules: a prespecifi ed analysis of data from the NELSON trial of low-dose CT screening
Nanda Horeweg et al.
LANCET ONCOLOGY (2014)
Automatic detection of subsolid pulmonary nodules in thoracic computed tomography images
Colin Jacobs et al.
MEDICAL IMAGE ANALYSIS (2014)
Lung Cancer in China Challenges and Interventions
Jun She et al.
CHEST (2013)
Annual or biennial CT screening versus observation in heavy smokers: 5-year results of the MILD trial
Ugo Pastorino et al.
EUROPEAN JOURNAL OF CANCER PREVENTION (2012)
A unified framework for population-based metaheuristics
Bo Liu et al.
ANNALS OF OPERATIONS RESEARCH (2011)
Crowd sourcing in drug discovery
Monika Lessl et al.
NATURE REVIEWS DRUG DISCOVERY (2011)
A novel computer-aided lung nodule detection system for CT images
Maxine Tan et al.
MEDICAL PHYSICS (2011)
The Lung Image Database Consortium, (LIDC) and Image Database Resource Initiative (IDRI): A Completed Reference Database of Lung Nodules on CT Scans
Samuel G. Armato et al.
MEDICAL PHYSICS (2011)
Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening
Denise R. Aberle et al.
NEW ENGLAND JOURNAL OF MEDICINE (2011)
Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: The ANODE09 study
Bram van Ginneken et al.
MEDICAL IMAGE ANALYSIS (2010)
The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A Completed Public Database of CT Scans for Lung Nodule Analysis
S. Armato et al.
MEDICAL PHYSICS (2010)
Shape-Based Computer-Aided Detection of Lung Nodules in Thoracic CT Images
Xujiong Ye et al.
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING (2009)
The Danish Randomized Lung Cancer CT Screening Trial-Overall Design and Results of the Prevalence Round
Jesper H. Pedersen et al.
JOURNAL OF THORACIC ONCOLOGY (2009)
Design, recruitment and baseline results of the ITALUNG trial for lung cancer screening with low-dose CT
Andrea Lopes Pegna et al.
LUNG CANCER (2009)
A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification
K. Murphy et al.
MEDICAL IMAGE ANALYSIS (2009)
Management of Lung Nodules Detected by Volume CT Scanning
Rob J. van Klaveren et al.
NEW ENGLAND JOURNAL OF MEDICINE (2009)
Reducing the dimensionality of data with neural networks
G. E. Hinton et al.
SCIENCE (2006)
A fast learning algorithm for deep belief nets
Geoffrey E. Hinton et al.
NEURAL COMPUTATION (2006)
Computer-aided diagnosis of localized ground-glass opacity in the lung at CT: Initial experience
KG Kim et al.
RADIOLOGY (2005)
Computer-aided diagnostic scheme for distinction between benign and malignant nodules in thoracic low-dose CT by use of massive training artificial neural network
K Suzuki et al.
IEEE TRANSACTIONS ON MEDICAL IMAGING (2005)
Toward automated segmentation of the pathological lung in CT
I Sluimer et al.
IEEE TRANSACTIONS ON MEDICAL IMAGING (2005)
Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography
K Suzuki et al.
MEDICAL PHYSICS (2003)
Lung cancer screening with CT: Mayo Clinic experience
SJ Swensen et al.
RADIOLOGY (2003)
Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images
SY Hu et al.
IEEE TRANSACTIONS ON MEDICAL IMAGING (2001)