# BTDNet: A Multi-Modal Approach for Brain Tumor Radiogenomic Classification
## 4 Experimental Results
### Ablation Study
By incorporating multiple modalities, our method could access a richer and more diverse set of data, leading to a more comprehensive understanding of the targeted problem. Additionally, our multimodal method likely reduced redundancy and ambiguity in the data interpretation, becoming more robust and less susceptible to errors or noise. Finally, in some cases, where there were limited data available in a single modality, the multimodal approach helped mitigate data scarcity issues by leveraging information from other modalities. In the following, we perform an ablation study with regard to the proposed data augmentation strategies. In more detail, Table 2 shows the performance of our method when (i) no test-time data augmentation was performed (i.e., during inference, the method was evaluated only on the real non-augmented data, and three augmented versions of the data were not created and used in the evaluation); (ii) no geometric transformations were used (i.e., both random horizontal flip and rotation were not performed on the data during training); and (iii) no MixAugment was used (i.e., the method was trained only using the real training data and during training, no virtual data were constructed and subsequently used along with the real data). It can be seen that in all cases, the performance \begin{table}
\begin{tabular}{c c} \hline \hline
**BTDNet** & **\(\mathbf{F_{1}}\) Score** \\ \hline ResNet50 as CNN & 64.1 \(\pm\) 3.6 \\ ResNet101 as CNN & 62.8 \(\pm\) 3.88 \\ EfficientNetB0 as CNN & 63.9 \(\pm\) 3.92 \\ EfficientNetB3 as CNN & 62.9 \(\pm\) 4.21 \\ ConvNeXt-T as CNN & 64.9 \(\pm\) 3.7 \\ \hline LSTM, 64 units as RNN & 65.1 \(\pm\) 3.5 \\ LSTM, 256 units as RNN & 64.3 \(\pm\) 4.3 \\ GRU, 64 units as RNN & 65 \(\pm\) 3.7 \\ GRU, 256 units as RNN & 64.4 \(\pm\) 4.4 \\ \hline no routing component & 60.9 \(\pm\) 5.7 \\ no mask layer & 62.9 \(\pm\) 5.2 \\ dense layer (routing component), 32 units & 65.1 \(\pm\) 3.4 \\ dense layer (routing component), 128 units & 64.9 \(\pm\) 3.6 \\ \hline only FLAIR modality & 64.5 \(\pm\) 3.3 \\ only T1 wodality & 63.2 \(\pm\) 4.3 \\ only T1 wCE modality & 63.8 \(\pm\) 4.1 \\ only T2 modality & 64.1 \(\pm\) 3.8 \\ \hline no test-time data augmentation & 65.2 \(\pm\) 3.4 \\ no geometric transformations & 65.3 \(\pm\) 3.3 \\ no MixAugment & 64.7 \(\pm\) 3.8 \\ \hline categorical cross-entropy as objective function & 64.9 \(\pm\) 3.4 \\ binary cross-entropy as objective function & 64.5 \(\pm\) 3.5 \\ \hline
**BTDNet** & **66.2 \(\pm\) 3.1** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Ablation study evaluating the contribution of each component of BTDNet on the RSNA-ASNR-MICCAI BraTS 2021 Challenge dataset. of our method decreased, verifying that each data augmentation strategy alone resulted in a performance gain, and the best performance was achieved when all were used together. The biggest decrease in performance was observed when the MixAugment strategy was not used. It should also be noted that we performed one more experiment that is not shown so as not to clutter the results. We performed only geometric transformations (i.e., random horizontal flip and rotation), and the same transformation was performed on the slices within the same volume. Our method's performance in this case was 0.8% lower than its corresponding performance when a different random geometric transformation was performed on the slices within the same volume. Finally, the last ablation experiment concerns the contribution of the objective function. We experimented with using the proposed multi-class focal loss, as well as the standard categorical cross-entropy (with two outputs in the method) and binary cross-entropy (with one output in the method) losses. Table 2 shows that when the multi-class focal loss was utilized, our method achieved the best performance, which was at least 1.3% higher than the corresponding results when the other losses were used.