과제정보
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIT) (No. 2019R1C1C100904413).
참고문헌
- Coleman RE. Metastatic bone disease: clinical features, pathophysiology and treatment strategies. Cancer Treat Rev 2001;27:165-176 https://doi.org/10.1053/ctrv.2000.0210
- Levack P, Graham J, Collie D, Grant R, Kidd J, Kunkler I, et al. Don't wait for a sensory level--listen to the symptoms: a prospective audit of the delays in diagnosis of malignant cord compression. Clin Oncol (R Coll Radiol) 2002;14:472-480 https://doi.org/10.1053/clon.2002.0098
- Haanen JB, Robert C. Immune checkpoint inhibitors. Prog Tumor Res 2015;42:55-66 https://doi.org/10.1159/000437178
- Weber KL, Lewis VO, Randall RL, Lee AK, Springfield D. An approach to the management of the patient with metastatic bone disease. Instr Course Lect 2004;53:663-676
- Ait Skourt B, El Hassani A, Majda A. Lung CT image segmentation using deep neural networks. Procedia Comput Sci 2018;127:109-113 https://doi.org/10.1016/j.procs.2018.01.104
- Chen H, Dou Q, Yu L, Qin J, Heng PA. VoxResNet: deep voxelwise residual networks for brain segmentation from 3D MR images. Neuroimage 2018;170:446-455 https://doi.org/10.1016/j.neuroimage.2017.04.041
- Lee H, Tajmir S, Lee J, Zissen M, Yeshiwas BA, Alkasab TK, et al. Fully automated deep learning system for bone age assessment. J Digit Imaging 2017;30:427-441 https://doi.org/10.1007/s10278-017-9955-8
- Zhao Z, Pi Y, Jiang L, Xiang Y, Wei J, Yang P, et al. Deep neural network based artificial intelligence assisted diagnosis of bone scintigraphy for cancer bone metastasis. Sci Rep 2020;10:17046
- Sim Y, Chung MJ, Kotter E, Yune S, Kim M, Do S, et al. Deep convolutional neural network-based software improves radiologist detection of malignant lung nodules on chest radiographs. Radiology 2020;294:199-209 https://doi.org/10.1148/radiol.2019182465
- Kim J, Min JH, Kim SK, Shin SY, Lee MW. Detection of hepatocellular carcinoma in contrast-enhanced magnetic resonance imaging using deep learning classifier: a multicenter retrospective study. Sci Rep 2020;10:9458
- Hille G, Dunnwald M, Becker M, Steffen J, Saalfeld S, Tonnies K. Segmentation of vertebral metastases in MRI using an U-Net like convolutional neural network. In: Handels H, Deserno T, Maier A, Maier-Hein K, Palm C, Tolxdorff T, eds. Bildverarbeitung fur die medizin 2019. Wiesbaden: Springer Vieweg, 2019:31-36
- Grovik E, Yi D, Iv M, Tong E, Rubin D, Zaharchuk G. Deep learning enables automatic detection and segmentation of brain metastases on multisequence MRI. J Magn Reson Imaging 2020;51:175-182 https://doi.org/10.1002/jmri.26766
- 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. 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. Cham: Springer, 2016:424-432
- Dice LR. Measures of the amount of ecologic association between species. Ecology 1945;26:297-302 https://doi.org/10.2307/1932409
- Sorensen TJ. A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on Danish commons [dissertation]. Copenhagen: I Kommission hos E. Munksgaard, 1948
- Wang J, Fang Z, Lang N, Yuan H, Su MY, Baldi P. A multi-resolution approach for spinal metastasis detection using deep Siamese neural networks. Comput Biol Med 2017;84:137-146 https://doi.org/10.1016/j.compbiomed.2017.03.024