과제정보
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (the Ministry of Science and ICT) (No. NRF-2020R1F1A1077320).
참고문헌
- Cheng PM, Montagnon E, Yamashita R, Pan I, Cadrin-Chenevert A, Perdigon Romero F, et al. Deep learning: an update for radiologists. Radiographics 2021;41:1427-1445
- McBee MP, Awan OA, Colucci AT, Ghobadi CW, Kadom N, Kansagra AP, et al. Deep learning in radiology. Acad Radiol 2018;25:1472-1480
- Rajpurkar P, Irvin J, Ball RL, Zhu K, Yang B, Mehta H, et al. Deep learning for chest radiograph diagnosis: a retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS Med 2018;15:e1002686
- Joo B, Ahn SS, Yoon PH, Bae S, Sohn B, Lee YE, et al. A deep learning algorithm may automate intracranial aneurysm detection on MR angiography with high diagnostic performance. Eur Radiol 2020;30:5785-5793
- Kuo W, Hane C, Mukherjee P, Malik J, Yuh EL. Expert-level detection of acute intracranial hemorrhage on head computed tomography using deep learning. Proc Natl Acad Sci USA 2019;116:22737-22745
- Willemink MJ, Koszek WA, Hardell C, Wu J, Fleischmann D, Harvey H, et al. Preparing medical imaging data for machine learning. Radiology 2020;295:4-15
- Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, et al. A guide to deep learning in healthcare. Nat Med 2019;25:24-29
- Park SH, Han K. Methodologic guide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis and prediction. Radiology 2018;286:800-809
- Candemir S, Nguyen XV, Folio LR, Prevedello LM. Training strategies for radiology deep learning models in data-limited scenarios. Radiol Artif Intell 2021;3:e210014
- Rauschecker AM, Gleason TJ, Nedelec P, Duong MT, Weiss DA, Calabrese E, et al. Interinstitutional portability of a deep learning brain MRI lesion segmentation algorithm. Radiol Artif Intell 2021;4:e200152
- Eche T, Schwartz LH, Mokrane FZ, Dercle L. Toward generalizability in the deployment of artificial intelligence in radiology: role of computation stress testing to overcome underspecification. Radiol Artif Intell 2021;3:e210097
- Yu AC, Mohajer B, Eng J. External validation of deep learning algorithms for radiologic diagnosis: a systematic review. Radiol Artif Intell 2022;4:e210064
- Lee SB, Cho YJ, Hong Y, Jeong D, Lee J, Kim SH, et al. Deep learning-based image conversion improves the reproducibility of computed tomography radiomics features: a phantom study. Invest Radiol 2022;57:308-317
- Chen C, Bai W, Davies RH, Bhuva AN, Manisty CH, Augusto JB, et al. Improving the generalizability of convolutional neural network-based segmentation on CMR images. Front Cardiovasc Med 2020;7:105
- Shi WZ, Caballero J, Huszar F, Totz J, Aitken AP, Bishop R, et al. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network.com Web site. https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Shi_Real-Time_Single_Image_CVPR_2016_paper.pdf. Published September 23, 2016. Accessed December 20, 2021
- Wang X, Xie L, Dong C, Shan Y. Real-ESRGAN: training realworld blind super-resolution with pure synthetic data.com Web site. https://openaccess.thecvf.com/content/ICCV2021W/AIM/papers/Wang_Real-ESRGAN_Training_Real-World_Blind_Super-Resolution_With_Pure_Synthetic_Data_ICCVW_2021_paper.pdf. Published August 17, 2021. Accessed December 20, 2021
- Johnson J, Alahi A, Fei-Fei L. Perceptual losses for real-time style transfer and super-resolution.com Web site. https://doi.org/10.1007/978-3-319-46475-6_43. Published March 27, 2016. Accessed December 20, 2021
- Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. Generative adversarial nets.com Web site. https://papers.nips.cc/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf. Published June 10, 2014. Accessed December 20, 2021
- Ledig C, Theis L, Huszar F, Caballero J, Cunningham A, Acosta A, et al. Photo-realistic single image super-resolution using a generative adversarial network.com Web site. https://openaccess.thecvf.com/content_cvpr_2017/papers/Ledig_Photo-Realistic_Single_Image_CVPR_2017_paper.pdf. Published May 25, 2017. Accessed December 20, 2021
- Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 [Preprint]. [posted September 4, 2014; revised April 10, 2015; cited December 22, 2021] https://arxiv.org/abs/1409.1556
- Kingma DP, Ba J. Adam: A method for stochastic optimization. arXiv:1412.6980 [Preprint]. [posted December 22, 2014; revised January 30, 2017; cited December 22, 2021] https://doi.org/10.48550/arXiv.1412.6980
- Lin LI. A concordance correlation coefficient to evaluate reproducibility. Biometrics 1989;45:255-268
- Bluemke DA, Moy L, Bredella MA, Ertl-Wagner BB, Fowler KJ, Goh VJ, et al. Assessing radiology research on artificial intelligence: a brief guide for authors, reviewers, and readers-from the radiology editorial board. Radiology 2020;294:487-489
- van Winkel SL, Rodriguez-Ruiz A, Appelman L, Gubern-Merida A, Karssemeijer N, Teuwen J, et al. Impact of artificial intelligence support on accuracy and reading time in breast tomosynthesis image interpretation: a multi-reader multi-case study. Eur Radiol 2021;31:8682-8691
- Quon JL, Han M, Kim LH, Koran ME, Chen LC, Lee EH, et al. Artificial intelligence for automatic cerebral ventricle segmentation and volume calculation: a clinical tool for the evaluation of pediatric hydrocephalus. J Neurosurg Pediatr 2020;27:131-138
- Winkel DJ, Wetterauer C, Matthias MO, Lou B, Shi B, Kamen A, et al. Autonomous detection and classification of PI-RADS lesions in an MRI screening population incorporating multicenter-labeled deep learning and biparametric imaging: proof of concept. Diagnostics (Basel) 2020;10:951
- Kawaguchi K, Kaelbling LP, Bengio Y. Generalization in deep learning. arXiv:1710.05468 [Preprint]. [posted October 16, 2017; revised December 11, 2017; cited December 22, 2021] https://doi.org/10.48550/arXiv.1710.05468
- Futoma J, Simons M, Panch T, Doshi-Velez F, Celi LA. The myth of generalisability in clinical research and machine learning in health care. Lancet Digit Health 2020;2:e489-e492
- Bhuva AN, Bai W, Lau C, Davies RH, Ye Y, Bulluck H, et al. A multicenter, scan-rescan, human and machine learning CMR study to test generalizability and precision in imaging biomarker analysis. Circ Cardiovasc Imaging 2019;12:e009214
- Onofrey JA, Casetti-Dinescu DI, Lauritzen AD, Sarkar S, Venkataraman R, Fan RE, et al. Generalizable multi-site training and testing of deep neural networks using image normalization. Proc IEEE Int Symp Biomed Imaging 2019;2019:348-351
- Sanford TH, Zhang L, Harmon SA, Sackett J, Yang D, Roth H, et al. Data augmentation and transfer learning to improve generalizability of an automated prostate segmentation model. AJR Am J Roentgenol 2020;215:1403-1410
- Shorten C, Khoshgoftaar TM. A survey on image data augmentation for deep learning. J Big Data 2019;6:1-48