과제정보
All authors belong to Medical Imaging and Intelligent Reality Lab (MI2RL). We thank Dr. Yongsik Sim (Department of Radiology, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea), for development of two models that chest radiographs view classification model and enhancement classification.
참고문헌
- Kennedy AD, Leigh-Brown AP, Torgerson DJ, Campbell J, Grant A. Resource use data by patient report or hospital records: do they agree? BMC Health Serv Res 2002;2:2
- Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol 2017;2:230-243
- Shen D, Wu G, Suk HI. Deep learning in medical image analysis. Annu Rev Biomed Eng 2017;19:221-248 https://doi.org/10.1146/annurev-bioeng-071516-044442
- Hesamian MH, Jia W, He X, Kennedy P. Deep learning techniques for medical image segmentation: achievements and challenges. J Digit Imaging 2019;32:582-596 https://doi.org/10.1007/s10278-019-00227-x
- Hwang EJ, Nam JG, Lim WH, Park SJ, Jeong YS, Kang JH, et al. Deep learning for chest radiograph diagnosis in the emergency department. Radiology 2019;293:573-580 https://doi.org/10.1148/radiol.2019191225
- Weikert T, Rapaka S, Grbic S, Re T, Chaganti S, Winkel DJ, et al. Prediction of patient management in COVID-19 using deep learning-based fully automated extraction of cardiothoracic CT metrics and laboratory findings. Korean J Radiol 2021;22:994-1004 https://doi.org/10.3348/kjr.2020.0994
- Abouelmehdi K, Beni-Hessane A, Khaloufi H. Big healthcare data: preserving security and privacy. J Big Data 2018;5:1
- Lecun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proceedings of the IEEE 1998;86:2278-2324
- Deng J, Dong W, Socher R, Li L, Kai L, Fei-Fei L. ImageNet: a large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition; 2009 Jun 20-25; Miami, FL, USA: IEEE; 2009; p. 248-255
- Lin TY, Maire M, Belongie S, Hays J, Perona P, Ramanan D, et al. Microsoft coco: common objects in context. In: Fleet D, Pajdla T, Schiele B, Tuytelaars T, eds. Computer vision--ECCV 2014. Cham: Springer International Publishing, 2014:740-755
- Liu Z, Luo P, Wang X, Tang X. Deep learning face attributes in the wild. Proceedings of the IEEE International Conference on Computer Vision (ICCV); 2015 Dec 7-13; Washington, DC, USA: IEEE; 2015; p. 3730-3738
- Ke A, Ellsworth W, Banerjee O, Ng AY, Rajpurkar P. CheXtransfer: performance and parameter efficiency of ImageNet models for chest X-Ray interpretation. Proceedings of the Conference on Health, Inference, and Learning; 2021 Apr 8-10; Virtual: CHIL; 2021; p. 116-124
- Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells W, Frangi A, eds. Medical image computing and computer-assisted intervention - MICCAI 2015. MICCAI 2015. Lecture notes in computer science, vol 9351. Cham: Springer, 2015:234-241
- Cicek O, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O. 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin S, Joskowicz L, Sabuncu M, Unal G, Wells W, eds. Medical image computing and computer-assisted intervention - MICCAI 2016. MICCAI 2016. Lecture notes in computer science, vol 9901. Cham: Springer, 2016:424-432
- He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2016 Jun 27-30; Las Vegas, NV, USA: IEEE; 2016; p. 770-778
- Tan M, Pang R, Le QV. Efficientdet: scalable and efficient object detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2020 Jun 13-19; Seattle, WA, USA: IEEE; 2020; p. 10778-10787
- Tan M, Le Q. Efficientnet: rethinking model scaling for convolutional neural networks. Proceedings of the 36th International Conference on Machine Learning; 2019 Jun 9-15; Long Beach, CA, USA: PMLR; 2019; p. 6105-6114
- Shiraishi J, Katsuragawa S, Ikezoe J, Matsumoto T, Kobayashi T, Komatsu K, et al. Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists' detection of pulmonary nodules. AJR Am J Roentgenol 2000;174:71-74 https://doi.org/10.2214/ajr.174.1.1740071
- Heller N, Sathianathen N, Kalapara A, Walczak E, Moore K, Kaluzniak H, et al. The KiTS19 challenge data: 300 kidney tumor cases with clinical context, CT semantic segmentations, and surgical outcomes. arXiv preprint 2019;arXiv:1904.00445
- Marcus DS, Fotenos AF, Csernansky JG, Morris JC, Buckner RL. Open access series of imaging studies: longitudinal MRI data in nondemented and demented older adults. J Cogn Neurosci 2010;22:2677-2684
- Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, et al. Tensorflow: large-scale machine learning on heterogeneous distributed systems. arXiv preprint 2016;arXiv:1603.04467
- Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, et al. Pytorch: an imperative style, high-performance deep learning library. In: Wallach H, Larochelle H, Beygelzimer A, d'Alche-Buc F, Fox E, Garnett R, eds. Advances in neural information processing systems 32 (NeurIPS 2019). Vancouver: NeurIPS, 2019:8026-8037