과제정보
This research was supported by a grant (18173의료평331-1[DY0002258200]) from Ministry of Food and Drug Safety in 2020.
참고문헌
- Wichmann JL, Willemink MJ, De Cecco CN. Artificial intelligence and machine learning in radiology: current state and considerations for routine clinical implementation. Invest Radiol 2020;55:619-627 https://doi.org/10.1097/RLI.0000000000000673
- Data Bridge Market Research. Global artificial intelligence in medical imaging market-industry trends-forecast to 2026. Available at. https://www.databridgemarketresearch.com/reports/global-artificial-intelligence-in-medical-imaging-market. Accessed Sep 22, 2020
- 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 https://doi.org/10.1148/radiol.2020192224
- Oakden-Rayner L. Exploring large-scale public medical image datasets. Acad Radiol 2020;27:106-112 https://doi.org/10.1016/j.acra.2019.10.006
- 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 https://doi.org/10.1148/radiol.2017171920
- Weikert T, Cyriac J, Yang S, Nesic I, Parmar V, Stieltjes B. A practical guide to artificial intelligence-based image analysis in radiology. Invest Radiol 2020;55:1-7 https://doi.org/10.1097/RLI.0000000000000600
- Lloyd, K. Bias amplification in artificial intelligence systems. ArXiv preprint 2018;arXiv:1809.07842
- Yu AC, Eng J. One algorithm may not fit all: how selection bias affects machine learning performance. Radiographics 2020;40:1932-1937 https://doi.org/10.1148/rg.2020200040
- Song TJ, Fong Y, Cho SJ, Gonen M, Hezel M, Tuorto S, et al. Comparison of hepatocellular carcinoma in American and Asian patients by tissue array analysis. J Surg Oncol 2012;106:84-88 https://doi.org/10.1002/jso.23036
- Pasquinelli MM, Kovitz KL, Koshy M, Menchaca MG, Liu L, Winn R, et al. Outcomes from a minority-based lung cancer screening program vs the National Lung Screening Trial. JAMA Oncol 2018;4:1291-1293 https://doi.org/10.1001/jamaoncol.2018.2823
- Jaeger S, Candemir S, Antani S, Wang YX, Lu PX, Thoma G. Two public chest X-ray datasets for computer-aided screening of pulmonary diseases. Quant Imaging Med Surg 2014;4:475-477
- Pons E, Braun LM, Hunink MG, Kors JA. Natural language processing in radiology: a systematic review. Radiology 2016;279:329-343 https://doi.org/10.1148/radiol.16142770
- Gera S, Ettel M, Acosta-Gonzalez G, Xu R. Clinical features, histology, and histogenesis of combined hepatocellular-cholangiocarcinoma. World J Hepatol 2017;9:300-309 https://doi.org/10.4254/wjh.v9.i6.300
- Fasel JH, Selle D, Evertsz CJ, Terrier F, Peitgen HO, Gailloud P. Segmental anatomy of the liver: poor correlation with CT. Radiology 1998;206:151-156 https://doi.org/10.1148/radiology.206.1.9423665
- FDA. Artificial intelligence and machine learning in software as a medical device. Available at. https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-software-medical-device. Accessed Sep 24, 2020
- Azer SA. Deep learning with convolutional neural networks for identification of liver masses and hepatocellular carcinoma: a systematic review. World J Gastrointest Oncol 2019;11:1218-1230 https://doi.org/10.4251/wjgo.v11.i12.1218
- Wang W, Iwamoto Y, Han X, Chen YW, Chen Q, Liang D, et al. Classification of focal liver lesions using deep learning with fine-tuning. Proceedings of the 2018 International Conference on Digital Medicine and Image Processing; 2018 Nov; Okinawa, Japan: Association for Computing Machinery; 2018:56-60
- Liang D, Lin L, Hu H, Zhang Q, Chen Q, Han X, et al. Combining convolutional and recurrent neural networks for classification of focal liver lesions in multi-phase CT images. International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer 2018:666-675
- Das A, Acharya UR, Panda SS, Sabut S. Deep learning based liver cancer detection using watershed transform and Gaussian mixture model techniques. Cogn Syst Res 2019;54:165-175 https://doi.org/10.1016/j.cogsys.2018.12.009
- Gletsos M, Mougiakakou SG, Matsopoulos GK, Nikita KS, Nikita AS, Kelekis D. A computer-aided diagnostic system to characterize CT focal liver lesions: design and optimization of a neural network classifier. IEEE Trans Inf Technol Biomed 2003;7:153-162 https://doi.org/10.1109/TITB.2003.813793
- Cao SE, Zhang LQ, Kuang SC, Shi WQ, Hu B, Xie SD, et al. Multiphase convolutional dense network for the classification of focal liver lesions on dynamic contrast-enhanced computed tomography. World J Gastroenterol 2020;26:3660-3672 https://doi.org/10.3748/wjg.v26.i25.3660
- Xu Y, Lin L, Hu H, Wang D, Zhu W, Wang J, et al. Texture-specific bag of visual words model and spatial cone matching-based method for the retrieval of focal liver lesions using multiphase contrast-enhanced CT images. Int J Comput Assist Radiol Surg 2018;13:151-164 https://doi.org/10.1007/s11548-017-1671-9
- Mougiakakou SG, Valavanis IK, Nikita A, Nikita KS. Differential diagnosis of CT focal liver lesions using texture features, feature selection and ensemble driven classifiers. Artif Intell Med 2007;41:25-37 https://doi.org/10.1016/j.artmed.2007.05.002
- Nayantara PV, Kamath S, Manjunath KN, Rajagopal KV. Computer-aided diagnosis of liver lesions using CT images: a systematic review. Comput Biol Med 2020;127:104035
- Ben-Cohen A, Klang E, Diamant I, Rozendorn N, Raskin SP, Konen E, et al. CT image-based decision support system for categorization of liver metastases into primary cancer sites: initial results. Acad Radiol 2017;24:1501-1509 https://doi.org/10.1016/j.acra.2017.06.008
- Yasaka K, Akai H, Abe O, Kiryu S. Deep learning with convolutional neural network for differentiation of liver masses at dynamic contrast-enhanced CT: a preliminary study. Radiology 2018;286:887-896 https://doi.org/10.1148/radiol.2017170706
- Park HJ, Park B, Lee SS. Radiomics and deep learning: hepatic applications. Korean J Radiol 2020;21:387-401 https://doi.org/10.3348/kjr.2019.0752