Generative adversarial network based data augmentation and gender-last training strategy with application to bone age assessment.

• Bone age assessment (BAA) could attain state-of-the-art accuracy through pre-training via optimal data augmentation, and the training procedure with supervised training and gender last training strategy. • Data augmentation based on combination of optimal transport and generative adversarial network with cosine distance metric can enhance BAA better than existing data augmentation methods. • Traditional image processing can be employed to replace the manual delineation of references which are utilized to train deep learning. • It might be generally applicable for multi-label classification with the label carrying less information being supervised last in the network. Bone age assessment (BAA) is widely used in determination of discrepancy between skeletal age and chronological age. Manual approaches are complicated which require experienced experts, while existing automatic approaches are perplexed with small and imbalanced samples which is a big challenge in deep learning. In this study, we proposed a new deep learning based method to improve the BAA training in both pre-training and training architecture. In pre-training, we proposed a framework using a new distance metric of cosine distance in the framework of optimal transport for data augmentation (CNN-GAN-OTD). In the training architecture, we explored the order of gender label and bone age information, supervised and semi-supervised training. We found that the training architecture with the CNN-GAN-OTD based data augmentation and supervised gender-last classification with supervised Inception v3 network yielded the best assessment (mean average error of 4.23 months). The proposed data augmentation framework could be a potential built-in component of general deep learning networks and the training strategy with different label order could inspire more and deeper consideration of label priority in multi-label tasks. [ABSTRACT FROM AUTHOR]