## 6 Discussion
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training choices. Therefore, we carefully compared the models trained by these two ablation approaches (each using histogram equalization, global image, or local patches) and picked the best result. According to Table 4, after histogram equalization, the MAE improves from 0.813 to 0.6 for the global network, and the average improvement is about 0.2 years for the local and fusion networks. When the global-local feature fusion strategy is adopted, the predicted result improves from 0.6 to 0.444, an improvement of about 0.16 years. This ablation study proves that equalization, global, and local fusion can effectively collaborate to improve bone age assessment task results. The overall X-ray image is dark and the contrast is low, and histogram equalization can enhance the local contrast without affecting the overall contrast. After processing, the bone structure can be better displayed to obtain better details, which is friendly to network learning features. These local ROI features refer to the degree of development of the ossification centers of the phalanges, carpals, radius, and ulna, the criteria for TW3, which pediatricians focus on in the film reading. From Table 5, [PERSON] [14] was the first automated BAA work, they tested several network architectures for BoNet and then chose the best one as the final model for evaluation. The structure was mainly aimed at the extraction of middle and low-level visual features and had a non-rigid deformation denoising layer, but for X-rays, the noise was not a very fatal factor. Moreover, after many convolutions in deep learning, the feedback was more of deep-level image features; thus, the model lacks the learning and utilization of deep features, and cannot guarantee sufficient accuracy. [PERSON] [39] pointed out that all five winning algorithms used a preprocessing step, in which images were normalized or important anatomic areas were selected prior to algorithm training. Preprocessing seems to be an important component of the generalizability of the algorithm. The first place achieved an amazing effect of 0.37 years in the test set of 200 cases. This approach combined pixel and gender information and used an ensemble method to filter multiple high-performance models to improve the overall performance. However, the contestants claimed that there was a certain correlation in the testing set. [PERSON] [40] reconstructed bones using an active appearance model and predicted bone age based on shape, strength, and texture scores from the principal component analysis. It lacked robustness for the Chinese. Moreover, it requires high-quality X-ray images to obtain reliable results. In fact, it rejects images of poor quality or abnormal bone structure, for which cases, the analysis needs to be manual. It is a great challenge in clinical applications. Compared with the reconstructed bone image, the information of the raw image is more fidelity. [PERSON] [9] segmented hand bones based on morphological watersheds (downsampled by using bilinear interpolations on image pyramids); they used a spatial transformer to locate ROIs and retain positional parameters (attention mechanisms), and finally used ResNet as a backbone to predict the bone age. There was an error of 0.455 years, which reflected the strong learning ability of the transformer. The attention mechanism in BAA is less studied in the existing research, and [PERSON]'s work illustrates the effectiveness of the attention mechanism in this task. Moreover, we tested DenseNet, ResNet, and VGGNet separately, see Table 5; the complex deep networks DenseNet and ResNet do not perform well; in contrast, the simple structured VGGNet turned out to be good. The prediction results of the public dataset have also been improved to some extent, but the overall effect is lower than that of the in-house dataset. There are obvious racial differences between the two datasets. The public dataset is from the Children's Hospital Stanford and Colorado, involving many races, while the in-house dataset is for Chinese people.