Hosny A, Parmar C, Quackenbush J, et al. Artificial intelligence in radiology. Nat Rev Cancer 2018;18(8):500-10.
Liu X, Faes L, Kale AU, et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and
meta-analysis. Lancet Digital Health. 2019;1(6):271-97.
Dallora AL, Anderberg P, Kvist O, et al. Bone age assessment with various machine learning techniques: A systematic literature review and meta-analysis. PloS one 2019;14(7):220-42
Greulich WW, Pyle SI, Todd TW. Radiographic atlas of skeletal development of the hand and wrist. Stanford: Stanford
University Press, 1959;2:150-9.
Tanner JM, Whitehouse RH, Cameron N, et al. Assessment of skeletal maturity and prediction of adult height (TW2 method). London: Academic press 1975;16.
Anwar SM, Majid M, Qayyum A, et al. Medical image analysis using convolutional neural networks: a review. J Med Syst 2018;42(11):226.
Jang L, Kwanggi K. Applying Deep Learning in Medical Images: The Case of Bone Age Estimation. Health Inform Res 2018;24(1):86-92.
He K, Zhang X, Ren S, et al. Deep Residual Learning for Image Recognition. IEEE CVPR 2016; DOI: 10.1109/ CVPR.2016.90
Nguyen HD, Kim SH. Automatic whole-body bone age as- sessment using deep hierarchical features, 2019. (Accessed January 10, 2020, at https://arxiv.org/pdf/1901.10237v1.pdf)
Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition, 2014. (Accessed January10, 2020, at https://arxiv.org/pdf/1409.1556.pdf)
Jetley S, Lord NA, Lee N, et al. Learn to pay attention, 2018. (Accessed January10, 2020, at https://arxiv.org/ pdf/1804.02391v2.pdf).
Keatmanee C, Klabwong S, Osatavanichvong K, et al. Performance of convolutional neural networks and transfer learning for skeletal bone age assessment. BKK Med J 2019;15(1).
Yuanfeng Ji, Hao C, Dan L, et al. PRSNet: Part relation and selection network for bone age assessment, 2019. (Accessed January10, 2020, at https://arxiv.org/pdf/1909.05651.pdf).
Deng J, Dong W, Socher R, et al. Imagenet: A large-scale hierarchical image database. (Accessed January10, 2020, at http://www.image-net.org/papers/imagenet_cvpr09.pdf).
Tang W, Wu G, Shen G. Improved automatic radiographic bone age prediction with deep transfer learning. 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Suzhou, China, 2019:1-6. DOI: 10.1109/CISP- BMEI48845.2019.8965906
Zhou J, Li Z, Zhi W, et al. Dawes. Using convolutional neural networks and transfer learning for bone age classification. 2017International Conference on Digital Image Computing: Techniques and Applications (DICTA), Sydney, NSW, 2017:1-6. DOI: 10.1109/DICTA.2017.8227503
Pan SJ, Yang Q. A survey on transfer learning. IEEE Transactions on knowledge and data engineering 2009;22(10):1345-59.
Shorten C, Khoshgoftaar TM. A survey on image data augmentation for deep learning. J Big Data 2019;6(1): 60.
Wang J, Perez L. The effectiveness of data augmentation in image classification using deep learning. Convolutional Neural Networks Vis. Recognit, 2017:11.
Mikołajczyk A, Grochowski M. Data augmentation for improving deep learning in image classification problem. International interdisciplinary PhD workshop (IIPhDW), 2018:117-22.
Inoue H. Data augmentation by pairing samples for images classification, 2018. arXiv preprint arXiv:1801.02929.
Simonyan K, Vedaldi A, Zisserman A. Deep inside convolutional networks: Visualising image classification models and saliency maps, 2013. arXiv preprint arXiv: 1312.6034.
Zhou B, Khosla A, Lapedriza A, et al. Learning deep features for discriminative localization. In Proceedings of the IEEE conference on computer vision and pattern recognition, 2016:2921-9.
Kim I, Rajaraman S, Antani S. Visual interpretation of convolutional neural network predictions in Classifying Medical Image Modalities. Diagnostics 2019; 9(2):38.
Koitka S, Demircioglu A, Kim MS, et al. Ossification area localization in pediatric hand radiographs using deep neural networks for object detection. PloS one 2018;13(11):0207496