Electronic ISSN 2287-0237

VOLUME

LATEST UPDATE IN BONE AGE ASSESSMENT MODEL WITH DEEP LEARNING

SEPTEMBER 2020 - VOL.16 | REVIEWS ARTICLE
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  3. 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

  4. Greulich WW, Pyle SI, Todd TW. Radiographic atlas of skeletal development of the hand and wrist. Stanford: Stanford

    University Press, 1959;2:150-9.

  5. Tanner JM, Whitehouse RH, Cameron N, et al. Assessment of skeletal maturity and prediction of adult height (TW2 method). London: Academic press 1975;16.

  6. Anwar SM, Majid M, Qayyum A, et al. Medical image analysis using convolutional neural networks: a review. J Med Syst 2018;42(11):226.

  7. Jang L, Kwanggi K. Applying Deep Learning in Medical Images: The Case of Bone Age Estimation. Health Inform Res 2018;24(1):86-92.

  8. He K, Zhang X, Ren S, et al. Deep Residual Learning for Image Recognition. IEEE CVPR 2016; DOI: 10.1109/ CVPR.2016.90

  9. 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)

  10. 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)

  11. Jetley S, Lord NA, Lee N, et al. Learn to pay attention, 2018. (Accessed January10, 2020, at https://arxiv.org/ pdf/1804.02391v2.pdf).

  12. 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).

  13. 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).

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  16. 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

  17. Pan SJ, Yang Q. A survey on transfer learning. IEEE Transactions on knowledge and data engineering 2009;22(10):1345-59.

  18. Shorten C, Khoshgoftaar TM. A survey on image data augmentation for deep learning. J Big Data 2019;6(1): 60.

  19. Wang J, Perez L. The effectiveness of data augmentation in image classification using deep learning. Convolutional Neural Networks Vis. Recognit, 2017:11.

  20. Mikołajczyk A, Grochowski M. Data augmentation for improving deep learning in image classification problem. International interdisciplinary PhD workshop (IIPhDW), 2018:117-22.

  21. Inoue H. Data augmentation by pairing samples for images classification, 2018. arXiv preprint arXiv:1801.02929.

  22. Simonyan K, Vedaldi A, Zisserman A. Deep inside convolutional networks: Visualising image classification models and saliency maps, 2013. arXiv preprint arXiv: 1312.6034.

  23. 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.

  24. Kim I, Rajaraman S, Antani S. Visual interpretation of convolutional neural network predictions in Classifying Medical Image Modalities. Diagnostics 2019; 9(2):38.

  25. 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

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