# Learning Models for Bone Marrow Edema Detection in Magnetic Resonance Imaging
## 3 Results and Discussion  
From the experimental tune, for the high intensity masking technique, \(\alpha=1.5\times\sigma\) and \(\beta=2\times\sigma\) were selected. These thresholds were determined by visually assessing the final image with the goal of detecting the BME regions while avoiding the detection of noise and structures, such as the growth plate. Initially, experiments with hyperparameters were conducted to reach stable and efficient training. The results of these experiments were a learning rate of 0.001 and a batch size of 112 (due to computational limits), and the best optimizer was stochastic gradient descent with a momentum of 0. These hyperparameters were kept throughout the experiments, and cross-entropy was used as the loss function. To fine-tune the models combined with high intensity masking technique, the best result corresponds to freezing 6 of the 10 layers of the Resnet-18. This model achieved a balanced accuracy of 0.852 and an AUC of 0.964 (Table 3). The confusion matrix of the test set for the best predictive model is presented in Table 4. For the bone segmentation approach, the best result after fine-tuning the model was a balanced accuracy of 0.568 and an AUC of 0.550, achieved when freezing six layers. Fine-tuning the best model that uses high intensity masking leads to much better results; however, it is possible that these approaches are way too hindered by the small size of the datasets. The progressive unfreezing of layers during training, using the high intensity masking technique, achieved the highest validation balanced accuracy when six layers were frozen. After that mark, the validation balanced accuracy decreased, but that may be due to the model already being overfitted. The balanced accuracy on the test set improved massively due to the model now correctly predicting 8 of the 10 non-edema patients. This result was a big improvement compared to the model trained from scratch and had a very promising outcome. Considering the different regularization methods applied to overcome the evident overfitting effect during training, Table 3 suggests that only a specific combination is capable of achieving a satisfactory hypothesis concerning not only the search-space complexity of BME-related imaging patterns but also the number of trainable parameters. In related approaches, model-level regularization techniques are the most common choices to manage the overfitting problem (e.g., use of pre-trained networks [7], dropout [19], and weight decay [20]); however, our results show that even with strong attempts at this level, only a data-level regularization, such as the HIM, enabled \begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Predicted Class**} \\ \cline{3-4}  & & **Non-Edema** & **Edema** \\ \hline \multirow{2}{*}{True class} & Non-edema & 8 & 2 \\ \cline{2-4}  & Edema & 4 & 38 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Confusion matrix for the best predictive model. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Training** & **ROI** & **Balanced Accuracy** & **AUC** \\ \hline \multirow{2}{*}{Scratch} & Bone Region & 0.545 & 0.534 \\  & HIM & 0.550 & 0.971 \\ \hline \multirow{2}{*}{TL (fixed feature extractor)} & Bone Region & 0.583 & 0.618 \\  & HIM & 0.588 & 0.683 \\ \hline \multirow{2}{*}{TL (fine-tuned feature extractor)} & Bone Region & 0.568 & 0.550 \\  & HIM & **0.852** & **0.964** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the test set using different training strategies and two regions of analysis. the construction of a more generalizable model by significantly reducing the presence of noisy and irrelevant information. This result may create helpful implications for future works, in which data scarcity still prevents the application of deep learning solutions. In an attempt to more confidently assess the performance of the high intensity masking method, the dataset was shuffled five times to produce five different train/validation/test sets, and the Resnet-18 was trained and tested in each experiment.