Related references
Note: Only part of the references are listed.Quality of science and reporting of radiomics in oncologic studies: room for improvement according to radiomics quality score and TRIPOD statement
Ji Eun Park et al.
EUROPEAN RADIOLOGY (2020)
Radiomics signature: a biomarker for the preoperative discrimination of lung invasive adenocarcinoma manifesting as a ground-glass nodule
Li Fan et al.
EUROPEAN RADIOLOGY (2019)
Identifying EGFR mutations in lung adenocarcinoma by noninvasive imaging using radiomics features and random forest modeling
Tian-Ying Jia et al.
EUROPEAN RADIOLOGY (2019)
Radiomics signature: A potential and incremental predictor for EGFR mutation status in NSCLC patients, comparison with CT morphology
Wenting Tu et al.
LUNG CANCER (2019)
Deep Learning Predicts Lung Cancer Treatment Response from Serial Medical Imaging
Yiwen Xu et al.
CLINICAL CANCER RESEARCH (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)
Model-free prostate cancer segmentation from dynamic contrast-enhanced MRI with recurrent convolutional networks: A feasibility study
Peter Q. Lee et al.
COMPUTERIZED MEDICAL IMAGING AND GRAPHICS (2019)
Development and validation of a prognostic index for efficacy evaluation and prognosis of first-line chemotherapy in stage III-IV lung squamous cell carcinoma
Jiangdian Song et al.
EUROPEAN RADIOLOGY (2019)
Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning
Shuo Wang et al.
EUROPEAN RESPIRATORY JOURNAL (2019)
Development and Validation of a Deep Learning-Based Automated Detection Algorithm for Major Thoracic Diseases on Chest Radiographs
Eui Jin Hwang et al.
JAMA NETWORK OPEN (2019)
Radiomic signature as a diagnostic factor for histologic subtype classification of non-small cell lung cancer
Xinzhong Zhu et al.
EUROPEAN RADIOLOGY (2018)
Accuracy of an automated system for tuberculosis detection on chest radiographs in high-risk screening
J. Melendez et al.
INTERNATIONAL JOURNAL OF TUBERCULOSIS AND LUNG DISEASE (2018)
Deep learning for classifying fibrotic lung disease on high-resolution computed tomography: a case-cohort study
Simon L. F. Walsh et al.
LANCET RESPIRATORY MEDICINE (2018)
Deep Learning from Label Proportions for Emphysema Quantification
Gerda Bortsova et al.
MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2018, PT II (2018)
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
Vijay Badrinarayanan et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (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)
Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks
Paras Lakhani et al.
RADIOLOGY (2017)
Computer-aided detection (CADe) and diagnosis (CADx) system for lung cancer with likelihood of malignancy
Macedo Firmino et al.
BIOMEDICAL ENGINEERING ONLINE (2016)
Radiomics: Images Are More than Pictures, They Are Data
Robert J. Gillies et al.
RADIOLOGY (2016)
2015 RAD-AID Conference on International Radiology for Developing Countries: The Evolving Global Radiology Landscape
Andrew Kesselman et al.
JOURNAL OF THE AMERICAN COLLEGE OF RADIOLOGY (2016)
Automatic Tuberculosis Screening Using Chest Radiographs
Stefan Jaeger et al.
IEEE TRANSACTIONS ON MEDICAL IMAGING (2014)
Building a reference multimedia database for interstitial lung diseases
Adrien Depeursinge et al.
COMPUTERIZED MEDICAL IMAGING AND GRAPHICS (2012)
White Paper Report of the RAD-AID Conference on International Radiology for Developing Countries: Identifying Challenges, Opportunities, and Strategies for Imaging Services in the Developing World
Daniel J. Mollura et al.
JOURNAL OF THE AMERICAN COLLEGE OF RADIOLOGY (2010)
Classification of parenchymal abnormality in scleroderma lung using a novel approach to denoise images collected via a Multicenter study
Hyun J. Kim et al.
ACADEMIC RADIOLOGY (2008)
Share Paper
