# Segmentation of Intracranial Hemorrhage Using Semi-Supervised Multi-Task Attention-Based U-Net
## 6 Results  
In the experimental study, we evaluated the performance of the trained models in terms of both Dice and Jaccard coefficients and averaged the results for each individual run during cross-validation. The results, which are given in Table 1, show that our semi-supervised model beat all other modelstested, independently of the amount of data used to train them. However, it was interesting to see that the margin of performance gain decreased between the supervised and semi-supervised models as the amount of data used to train increased. This was because the semi-supervised methodology promoted feature learning through the reconstruction of the background and foreground after attention was introduced during a segmentation task.  
While [PERSON] et al. was the only other research team to train and test on the same dataset as our team; other teams have developed ICH segmentation models that operated on other datasets. In Table 2, we show the Dice (Jaccard) coefficients of our best performing model compared with some other models and the amount of training data used. While [PERSON] et al. [9] were able to get higher Dice and Jaccard coefficients than us, they did so with 160 times the training samples, which was an expected result due to the universal approximation theorem.  
To help us to appreciate the effectiveness of the proposed model, Figure 3 shows some examples of the ICH segmentation across the performance spectrum. The results were obtained by our semi-supervised model with 80% of data for training.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Used** & **20\%** & **50\%** & **80\%** \\ \hline [PERSON] et al. [2] & - & - & 0.31 (0.18) \\ [PERSON] et al. [34] & Diverged & Diverged & Diverged \\ Pretrained & 0.33 (0.20) & 0.44 (0.28) & 0.61 (0.44) \\ Supervised & 0.31 (0.18) & 0.42 (0.27) & 0.61 (0.44) \\ Semi-Supervised & **0.44 (0.28)** & **0.51 (0.34)** & **0.67 (0.50)** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dice (Jaccard) coefficients obtained by various methods trained with 20%, 50%, and 80% of ground truth data. The first row corresponds to the u-net employed in [PERSON] et al.; the second row corresponds to [PERSON] et al.’s algorithm applied directly on our two datasets; the third row corresponds to our modified u-net pretrained on the classification task from the RSNA dataset; the fourth row corresponds to our modified u-net trained only in a supervised fashion; and the fifth row corresponds to our model combining the modified u-net and unsupervised attention autoencoder with curriculum learning.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Training Samples** & **Dice (Jaccard)** \\ \hline [PERSON] et al. [2] & 254 & 0.31 (0.18) \\ [PERSON] et al. [9] & 40,000 \({}^{\ddagger}\) & 0.85 (0.74) \\ [PERSON] et al. [14] & 720 \({}^{\ddagger}\) & 0.65 (0.48) \\ [PERSON] et al. [21] & 6000 & 0.62 (0.45) \\ Proposed & 254 & 0.67 (0.50) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dice (Jaccard) coefficients obtained by various methods for semantic segmentation of ICHs along with the number of training samples used. [PERSON] et al. and [PERSON] et al. used different datasets from the one used by [PERSON] et al. and us. Most notably, our algorithm is shown to beat [PERSON] et al.’s algorithm despite only having access to a third of the labeled data. \({}^{\dagger}\) [PERSON]’s dataset composed of approximately 901 patients totaling approximately 40,000 images containing ICH. \({}^{\ddagger}\) [PERSON]’s dataset comprised of approximately 90 patients. With approximately 80 depth slices per 3D CT sample, we estimate the amount of information contained within his dataset to be equivalent to approximately 720 data points.