Electronic ISSN 2287-0237
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12 Kotsiantis S., Kanellopoulos D. and Pintelas, P. Handling imbalanced datasets: A review. GESTS International Transactions on Computer Science and Engineering. 2015;30:25-36.
13. Rawat, W. and Wang, Z. Deep Convolutional Neural Networks for Image Classification. A Comprehensive Review. Neural computation. 2017;29(9):2352-2449.
14. Sinno J. P. and Qiang Y. A Survery on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering [online]. [cited 2019 Jan 1]. Available from: https://www.cse.ust. hk/~qyang/Docs/2009/tkde_transfer_learning.pdf
15. He, K., Zhang, X., Ren, S., and Sun, J. Deep Residual Learning for Image Recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2016:770-778.
16. Christian S., Vincent V., Sergey I., et al. Rethinking the Inception Architecture for Computer Vision. The CVPR paper provided by the Computer Vision foundation [online]. [cited 2019 Jan 1]. Available from: https://www.cv-foundation.org/ openaccess/content_cvpr_2016/papers/Szegedy_Rethinking_ the_Inception_CVPR_2016_paper.pdf
17. Karen S. and Andrew Z. Very Deep Convolutional Networks for Large-Scale Image Recognition [online]. [cited 2019 Jan 1]. Available from: https://arxiv.org/abs/1409.1556
18. RSNA Pediatric Bone age challenge [online]. 2017 [cited 2017 Oct 7]. Available from: http://rsnachallenges.cloudapp.net/ competitions/4
19. LeCun Y. Learning invariant feature hierarchies. Computer vision ECCV 2012. Workshops and demonstrations. Springer Berlin Heidelberg, 2012.
20. Jaderberg M., Karen S., and Andrew Z. Spatial transformer networks. Advances in Neural Information Processing Systems. 2015.
11. Larson DB., Chen MC., Lungren MP., et al. Performance of a Deep-Learning Neural Network Model in Assessing Skeletal Maturity on Prediatric Hand Radiographs. Radiology. 2018;287(1):313-322.
12. Kotsiantis S., Kanellopoulos D. and Pintelas, P. Handling imbalanced datasets: A review. GESTS International Transactions on Computer Science and Engineering. 2015;30:25-36.
13. Rawat, W. and Wang, Z. Deep Convolutional Neural Networks for Image Classification. A Comprehensive Review. Neural computation. 2017;29(9):2352-2449.
14. Sinno J. P. and Qiang Y. A Survery on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering [online]. [cited 2019 Jan 1]. Available from: https://www.cse.ust. hk/~qyang/Docs/2009/tkde_transfer_learning.pdf
15. He, K., Zhang, X., Ren, S., and Sun, J. Deep Residual Learning for Image Recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2016:770-778.
16. Christian S., Vincent V., Sergey I., et al. Rethinking the Inception Architecture for Computer Vision. The CVPR paper provided by the Computer Vision foundation [online]. [cited 2019 Jan 1]. Available from: https://www.cv-foundation.org/ openaccess/content_cvpr_2016/papers/Szegedy_Rethinking_ the_Inception_CVPR_2016_paper.pdf
17. Karen S. and Andrew Z. Very Deep Convolutional Networks for Large-Scale Image Recognition [online]. [cited 2019 Jan 1]. Available from: https://arxiv.org/abs/1409.1556
18. RSNA Pediatric Bone age challenge [online]. 2017 [cited 2017 Oct 7]. Available from: http://rsnachallenges.cloudapp.net/ competitions/4
19. LeCun Y. Learning invariant feature hierarchies. Computer vision ECCV 2012. Workshops and demonstrations. Springer Berlin Heidelberg, 2012.
20. Jaderberg M., Karen S., and Andrew Z. Spatial transformer networks. Advances in Neural Information Processing Systems. 2015.
