# DeepSarc-US: A Deep Learning Framework for Assessing Sarcopenia Using Ultrasound Images
## 4 Discussion and Conclusions  
In this study, a method was developed for the measurement of QMT using US images acquired using a phased array probe in a clinical setting. QMT has been put forth as an objective biomarker for frailty and a diagnostic criterion for sarcopenia. A number of DL models, including ResNet101, DensNet121, ConvNext-B, ViT-B, ViT-L, MAE-B, MAE-L, SwinT-B, and SwinT-L, were compared to predict this measurement. First, there was no significant and consistent difference when comparing all the CNN-based models with the transformer-based models in predicting QMT. In the _IW-Regression_ models, as summarized in Table 2, ConvNext-B, a CNN-based model, achieved a median absolute error of 0.27, while of the transformer-based models, SwinT-B also achieved the same median absolute error of 0.27. Furthermore, upon conducting a significance test, as illustrated in Figure 4a, the observed difference between the two models was not statistically significant. In the _CW-Regression_ models, as depicted in the results presented in Table 2, ConvNext-B once again demonstrated a superior performance among the CNN-based models, achieving the lowest median absolute error of 0.23. Similarly, within the transformer-based models, SwinT-B and SwinT-L closely followed, with median absolute errors of 0.24 and 0.25, respectively, showing competitive results comparable to ConvNext-B. Therefore, it can be concluded that both CNN- and transformer-based models exhibit a satisfactory performance when adequately trained. Second, performance improved for the _CW-Regression_ models compared to the _IW-Regression_ models for the task of QMT prediction. In the domain of CNN-based models, all the models experienced improvements through the utilization of the _CW-Regression_ strategy. To this end, ResNet101, DensNet121, and ConvNext-B demonstrated improvements of 0.1 cm, 0.12 cm, and 0.04 cm, respectively. Similarly, within the transformer-based models, the implementation of the proposed _CW-Regression_ strategy generally resulted in enhanced performance. Excluding MAE-L, which showed no improvements, ViT-B, ViT-L, MAE-B, SwinT-B, and SwinT-L achieved improvements of 0.03 cm, 0.03 cm, 0.03 cm, and 0.05 cm, respectively. These findings highlight the fact that for those models that are harder to train \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Median of Absolute Errors (cm)** & \\ \hline _Seg-Regression_ & _IW-Regression_ & _CW-Regression_ \\ \hline
0.13 & 0.28 ** & 0.25 ** \\ \hline \hline \end{tabular}  
* denotes a statistically significant differences with \(p\)-values \(<\) 0.01 between _Seg-Regression_ and two other methods.  
\end{table} 
Table 4: Median of absolute errors in QMT estimations. using small datasets, simplifying the task (i.e., pretraining on the classification task first) is highly beneficial for their performance. This is in line with a large body of evidence in curriculum learning [56] that has shown that training deep models on easier tasks first leads to better results. This is often due to the non-convex nature of the loss landscape, where training on an easy task helps the network to not be trapped in poor local minima. Given that transformer-based models have shown a large potential in the natural language processing field, our proposed _CW-Regression_ transformer models will be advantageous for US images where there are limited images available. Third, our experiments on the classification of QMT, i.e., the _IW-Classification_, and _RW-Classification_ models, showed that prior pretraining on a regression task is not beneficial for the classification models, which emphasizes the fact that regression is a harder task compared to classification. When compared to the _IW-Classification_ models, the _RW-Classification_ models generally displayed decreased performance across all eight SOTA models. Furthermore, there was no significant difference between the activation maps of the _IW-Classification_ and _RW-Classification_ models.