# A Cascade Model with Prior Knowledge for Bone Age Assessment
###### Abstract
In addition, some researchers have used DL models instead of ML methods for background denoising, improving accuracy while eliminating manual operations. Among them, [PERSON] et al. [20] used the Visual Geometry Group network (VGG) and the mask region-based convolutional neural network (Mask R-CNN) to extract hand regions for background denoising. [PERSON] et al. [21] proposed a multichannel evaluation model that preprocesses images to extract coarse and fine attention maps with a multichannel input regression network. [PERSON] et al. [22] extracted 17 Rols of the hand using Residual Network (ResNet), and the extracted RoIs were input into the Spatial Transformer Network (STN) for bone age assessment. Overall, using a deep learning model to solve the BAA problem can improve the accuracy of the evaluation process, shorten the evaluation time, and avoid the inconsistency between evaluation results caused by subjective factors. However, the above model is based on children from the same countries/regions. Due to the different characteristics of hand bone development in different countries/regions, models need to be experimented on different regional datasets to demonstrate the generalisability of the model. Most DL models treat BAA as a regression or multiclass classification problem at present, and most of the models were built based on data from populations different from Chinese, which may be less applicable to contemporary Chinese children. Clinically, the development of hand bones exhibits the characteristics of continuity and nonlinearity [13]. A regression network excessively simplifies the entire developmental process and does not consider the nonlinearity caused by the different developmental rates experienced by hand bones at each stage, rendering the network vulnerable to the influence of outliers and prone to cause overfitting in training [23]. A classification network ignores the continuity caused by the similarity among the forms of hand bones at adjacent ages; however, the use of ordered labels allows the model to achieve a good assessment [24]. In order to improve the above-mentioned shortcomings and limitations, this paper proposes a cascade model fusing of prior knowledge for BAA. The main contributions of this paper are as follows:  
* The bone age label is redefined by transforming it into a multi-point distribution label with semantic information. The proposed label, which considers the nonlinear and continuous characteristics, is embedded in the network through a cascade model;
* Base on a data sample of contemporary Chinese adolescents, the Rols of the RUS-CHN method is applied to the model as prior knowledge to improve accuracy;
* A new feature extractor is proposed based on MobileNet v2, which can capture more detailed features of hand bone;
* The network is trained with an improved Focal loss function to prevent model's overfitting and to address the impact of uneven data distribution on the network. This paper proposes a new representation of bone age labels and embeds it into the network through a cascade model to address the problem that the existing models did not consider nonlinearity and continuity simultaneously. In addition, the model fuses RoIs of RUS-CHN as prior knowledge, improving the adaptability of the model to children in the Chinese region. The rest of this paper is organized as follows. Section 2 presents details of our proposed model and the dataset. Section 3 demonstrates the model's assessment results. Section 4 provides relevant discussion and analysis of the strengths and weaknesses of our model, followed by conclusions in Section 5.