# A Cascade Model with Prior Knowledge for Bone Age Assessment
## 3 Results
### Evaluation of Bone Age Assessment
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Improved Feature Extractor** & & & & & & & & \\ \hline
**CBAM** & **k: 5\(\times\)5** & & & & & & & \\ \hline ✓ & & & & 5.05 & 66.11\% & 92.23\% & 98.81\% \\  & ✓ & & & 5.53 & 62.51\% & 90.79\% & 98.61\% \\ ✓ & ✓ & & & 5.01 & 66.67\% & 92.25\% & 98.65\% \\  & & ✓ & & 4.86 & 71.11\% & 93.33\% & 98.67\% \\  & & ✓ & ✓ & 4.72 & 74.81\% & 93.60\% & 98.67\% \\ ✓ & ✓ & ✓ & ✓ & 4.53 & 78.15\% & 94.04\% & 99.34\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: BAA model ablation experiments. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**O** & **H** & **C** & **P** & **MAE** & \(\pm\)**0.5** & \(\pm\)**1** & \(\pm\)**2** \\ \hline ✓ & & & & 7.21 & 56.82\% & 79.23\% & 96.86\% \\  & ✓ & & & 6.45 & 58.86\% & 83.33\% & 97.22\% \\  & ✓ & ✓ & & 6.31 & 58.75\% & 86.71\% & 97.92\% \\  & ✓ & ✓ & ✓ & 6.17 & 59.01\% & 88.89\% & 98.31\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Test results for different inputs base on baseline models. this increase in the size of the convolutional kernel can expand the range of the RF, thus enhancing the local feature extraction capability of the model, which can extract more local features of the hand bone and improve the performance of the model. Meanwhile, the adaptive assignment of channel weights by CAM allows the model to automatically place larger weights on the more important RoIs channels, thus improving the evaluation accuracy, and the SAM module enables the model to focus attention on the spatial region of the hand bone, further improving evaluation accuracy. After adding Multi-point distributed bone age labels containing rich semantic information to the model, compared to the baseline model, MAE decreased by 1.31 and accuracy is improved by 12.1%, 4.44%, and 0.36% for ground truth within \(\pm\)0.5 year, \(\pm\)1 year, and \(\pm\)2 year, respectively. It is shown that the proposed multi-point distribution bone age label, which well interprets the characteristics of skeletal development, contains the semantic information of classification, regression, and distribution learning within the label such that the model fully utilizes the label information and achieves a better assessment effect. In addition, we add the gender information to the network, and MAE decreased by 0.08 and accuracy is improved by 3.70% and 0.27% for ground truth within \(\pm\)0.5 year and \(\pm\)1 year, respectively. This is because adding gender information to the bone age label can further enrich the semantic information of the label, allowing the model to learn more useful information to help bone age assessment. **Loss function comparison experiment. ** In order to verify the effectiveness of the improved label distribution learning loss function, we conducted ablation experiments. At the same time, we compared it with the commonly used probability distribution learning loss function Kullback-Leibler Divergence (KL), and the experimental results are shown in Table 5. Note that the loss functions of the regression layers are all using the MAE. From the Table 5, compared to KL, using the FL cascade to train the model, MAE decreased by 0.16 and the accuracy is improved by 2.83%, 0.33%, and 0.54% for ground truth within \(\pm\)0.5 year, \(\pm\)1 year, and \(\pm\)2 year, respectively. We believe this is because FL is more applicable to data with uneven sample distribution such as medical datasets, which enables the model to adaptively adjust the weights of difficult samples, thus focusing the model's attention on difficult sample discovery and improving the model recognition performance. In addition, evaluation performance is further enhanced by the combined loss function of FL + LS, MAE decreased by 0.09, and accuracy is improved by 1.32% and 0.66% for ground truth within \(\pm\)0.5 year and \(\pm\)1 year, respectively. That is, the proposed loss function can improve the uneven distribution of samples in medical data sets and the characteristics of overfitting caused by the small amount of data to a certain extent.