Performance of deep learning for differentiating pancreatic diseases on contrast-enhanced magnetic resonance imaging: A preliminary study

Diagnostic and interventional imaging - Tập 101 - Trang 91-100 - 2020
X. Gao1,2, X. Wang1,2
1Shanghai Institute of Medical Imaging, 200032 Shanghai, China
2Department of Interventional Radiology, Fudan University Zhongshan Hospital, 200032 Shanghai, China

Tài liệu tham khảo

Lee, 2017, Deep learning in medical imaging: general overview, Korean J Radiol, 18, 570, 10.3348/kjr.2017.18.4.570 Chartrand, 2017, Deep learning: a primer for radiologists, Radiographics, 37, 2113, 10.1148/rg.2017170077 Erickson, 2018, Deep learning in radiology: does one size fit all?, J Am Coll Radiol, 15, 521, 10.1016/j.jacr.2017.12.027 Litjens, 2017, A survey on deep learning in medical image analysis, Med Image Anal, 42, 60, 10.1016/j.media.2017.07.005 Beregi, 2018, Radiology and artificial intelligence: an opportunity for our specialty, Diagn Interv Imaging, 99, 677, 10.1016/j.diii.2018.11.002 Ambroise Grandjean, 2018, Artificial intelligence assistance for fetal head biometry: assessment of automated measurement software, Diagn Interv Imaging, 99, 709, 10.1016/j.diii.2018.08.001 Cicero, 2017, Training and validating a deep convolutional neural network for computer-aided detection and classification of abnormalities on frontal chest radiographs, Invest Radiol, 52, 281, 10.1097/RLI.0000000000000341 Lakhani, 2017, Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks, Radiology, 284, 574, 10.1148/radiol.2017162326 Cheng, 2016, Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans, Sci Rep, 6, 24454, 10.1038/srep24454 Ribli, 2018, Detecting and classifying lesions in mammograms with deep learning, Sci Rep, 8, 4165, 10.1038/s41598-018-22437-z Herent, 2019, Detection and characterization of MRI breast lesions using deep learning, Diagn Interv Imaging, 100, 219, 10.1016/j.diii.2019.02.008 Schmauch, 2019, Diagnosis of focal liver lesions from ultrasound using deep learning, Diagn Interv Imaging, 100, 227, 10.1016/j.diii.2019.02.009 Yasaka, 2018, Deep learning with convolutional neural network for differentiation of liver masses at dynamic contrast-enhanced CT: a preliminary study, Radiology, 286, 887, 10.1148/radiol.2017170706 Liu, 2018, Deep learning approach for evaluating knee MR images: achieving high diagnostic performance for cartilage lesion detection, Radiology, 289, 160, 10.1148/radiol.2018172986 Krizhevsky, 2012, ImageNet classification with deep convolutional neural networks, 1097 Haixiang, 2017, Learning from class-imbalanced data: review of methods and applications, Expert Syst Appl, 73, 220, 10.1016/j.eswa.2016.12.035 Buda, 2018, A systematic study of the class imbalance problem in convolutional neural networks, Neural Netw, 106, 249, 10.1016/j.neunet.2018.07.011 Rawla, 2019, Epidemiology of pancreatic cancer: global trends, etiology and risk factors, World J Oncol, 10, 10, 10.14740/wjon1166 Zerbi, 2010, Clinicopathological features of pancreatic endocrine tumors: a prospective multicenter study in Italy of 297 sporadic cases, Am J Gastroenterol, 105, 1421, 10.1038/ajg.2009.747 Dasari, 2017, Trends in the incidence, prevalence, and survival outcomes in patients with neuroendocrine tumors in the United States, JAMA Oncol, 3, 1335, 10.1001/jamaoncol.2017.0589 Radford, 2015 Szegedy, 2016 Deng, 2009, 248 Sokolova, 2009, A systematic analysis of performance measures for classification tasks, Infor Process Manag, 45, 427, 10.1016/j.ipm.2009.03.002 Wei R, Wang J, Jia W. multiROC: calculating and visualizing ROC and PR curves across multi-class classifications, 2018. https://cran.r-project.org/web/packages/multiROC/index.html [Accessed 30 April 2019]. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Receiver operating characteristic (ROC), 2011. https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html [Accessed 30 April 2019]. Abadi, 2016 Kingma, 2014 Gulshan, 2016, Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs, JAMA, 316, 2402, 10.1001/jama.2016.17216 Goodfellow, 2014 Hussain, 2018, Differential data augmentation techniques for medical imaging classification tasks, AMIA Annu Symp Proc, 2017, 979 Taylor, 2017 Yasaka, 2018, Liver fibrosis: deep convolutional neural network for staging by using gadoxetic acid-enhanced hepatobiliary phase MR Images, Radiology, 287, 146, 10.1148/radiol.2017171928 Larson, 2018, Performance of a deep-learning neural network model in assessing skeletal maturity on pediatric hand radiographs, Radiology, 287, 313, 10.1148/radiol.2017170236