# A Cascade Model with Prior Knowledge for Bone Age Assessment
## 4 Discussion  
Based on the skeletal growth and development characteristics of Chinese adolescents and children since the start of the century, this paper proposes a cascade model that fuses prior knowledge. First, the model is characterized by the simultaneous use of classification,  
Figure 10: Bland–Altman plot for the predicted bone age. Figure 9: Regression Fitting Plot. A straight line represents ground truth, and discrete points represent predicted bone age values. regression, and distribution learning. Compared to traditional classification or regression models, the proposed model combines the nonlinearity and continuity of hand bone development. Second, the feature extractor of the model is improved based on the baseline network, which allows the model to extract more hand bone features and, thus, improves the evaluation accuracy. Third, the RoIs of the RUS-CHN method are considered prior knowledge in the network, which also improve the accuracy of the model. The final MAE of the model is 4.4 months, and the accuracy of the assessment within one year of the ground truth is 94.04%. On the one hand, aiming at the problem that the traditional network does not take the nonlinearity and continuity of bone growth and development into account, a novel bone age representation is proposed, where the semantic information is embedded in the BAA task using a cascade model. In detail, a fully connected layer with a semantic distribution of 216 neurons (corresponding to each month in the age range of 0-18 years) is inserted between the feature and regression layers. A multi-point distribution bone age label is output between the fully connected layer and the regression layer, and this label subsequently passes through the regression layer to obtain the corresponding predicted bone age value. A single digital bone age label is converted into 216 discrete and adjacent bins. Because the bins are discrete, each month of bone development between the ages of 0 and 18 is regarded as an independent individual, thereby taking the phased characteristics into account. At the same time, the adjacent bins between bins can be regarded as relatively continuous, as in the development ages of hand bones in adjacent months, which are independent of each other but have relative continuity. In brief, this design more closely reflects the reality of skeletal development, thus improving the consistency of the model assessment. On the other hand, the feature extractor is improved in three aspects, namely channel, spatial, and RF. Among them, the weight sizes of the channels where the three RoIs are located are newly assigned by CAM so that the model places a larger weight on top of the channel where evaluation is more meaningful. Moreover, the model's attention is spatially integrated by SAM to focus the model learning region on the hand bone region. In addition, increasing the RF size of the feature extractor allows the model to enhance the extraction of global information and obtain more global features. The three improvements enhance the feature extractor's ability to obtain valuable features, thus improving the accuracy of the evaluation. As to comparison with the most recent works [36; 37], our model considers the non-linearity and continuity simultaneously of skeletal development by converting the bone age label into a multi-point distribution label. In addition, we extracted the RoIs input model using YOLO v5, allowing the model to catch more detailed features and reach better assessment, with a 1.6 months reduction in MAE for our model compared to the work of [36]. In addition, the proposed network adopts target detection algorithms to localize the RoIs present in the RUS-CHN method and the region of the entire hand, and the corresponding regions of the three types of bones (hand, carpus, and phalanges) are extracted as prior knowledge, which can be subsequently learned by the model in a multichannel manner.