## 3 Results
### Lesion Detection  
Multiple experiments were conducted to select the best model, exploring different hyperparameters, augmentations, and MRI slice types. The best-performing model was trained using all available MRI sequences over a total of 8 h and 43 min. Table 4 presents the augmentations applied during training. These included common transformations such as image flipping, rotation, and resizing, along with contrast and brightness adjustments. The latter were particularly critical for ensuring robustness across the diverse MRI sequences (T1W, T2W, and T1W CE) included in the dataset. Additionally, moderate elastic transformations were applied to improve the model's ability to handle organ deformations visible in MRI studies.  
The best hyperparameters for the FCOS detection model are presented in Table 5. A small learning rate (0.0001) was chosen to ensure stable training, which is particularly important given the relatively modest size of our dataset, for deep learning standards.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Image Augmentations** & **Details** \\ \hline Resize Shortest Edge & Short edge range: 256–512 px, max size: 512 px \\ Horizontal Flip & Applied randomly with probability 50\% \\ Vertical Flip & Applied randomly with probability 50\% \\ Random Rotation & Angles: 0\({}^{\circ}\), 90\({}^{\circ}\) \\ Random Brightness Adjustment & Range: 70–150\% of original brightness \\ Random Contrast Adjustment & Range: 70–150\% of original contrast \\ Elastic Transform & Alpha: 50.0, Sigma: 12.0, Probability: 50\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Best data augmentations for training the lesion detection DL model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ground Truth/Prediction** & **Adenoma (Predicted)** & **Non-Adenoma (Predicted)** & **No Lesion (Predicted)** \\ \hline \multirow{3}{*}{Adenoma (GT)} & Lesion detected and & Lesion detected but & Lesion incorrectly \\  & classified as adenoma & misclassified as & not detected \\  & & non-adenoma & \\ \hline \multirow{3}{*}{Non-Adenoma (GT)} & Lesion detected but & Lesion detected and & Lesion incorrectly \\  & misclassified as adenoma & correctly classified as & not detected \\  & & non-adenoma & \\ \hline \multirow{3}{*}{No Lesion (GT)} & Lesion incorrectly detected & Lesion incorrectly detected & Lesion correctly \\  & and classified as adenoma & and classified as & not detected \\  & & non-adenoma & \\ \hline \end{tabular}
\end{table}
Table 3: Descriptive confusion matrix for end-to-end evaluation.  
The backbone architecture used in the FCOS detection model was ResNet-50, which was pre-trained on ImageNet. To improve training efficiency and mitigate overfitting, the first two stages of the ResNet backbone were frozen, allowing the higher-level layers to adapt to the specific features of MRI images. The focal loss parameters were left at their defaults. A detection score threshold of 0.5 was applied, balancing the trade-off between sensitivity and precision. Additionally, a slightly higher non-maximum suppression (NMS) threshold of 0.6 was used to retain more overlapping bounding boxes, which helped detect small or irregularly shaped lesions. This deliberate configuration reflects the need to prioritize lesion sensitivity while maintaining reliable performance across varying lesion characteristics.  
The model's performance metrics are detailed in Table 6. The AP of 42.68% demonstrates the model's capability to handle challenging detection scenarios, while an AP50 of 85.80% confirms its strong performance under less strict IoU thresholds. Additionally, traditional classification metrics, computed for an IoU threshold of 50%, showcase the model's high recall (89.47%) and F1-score (87.62%) while detecting adrenal lesions.