# Segmentation of Intracranial Hemorrhage Using Semi-Supervised Multi-Task Attention-Based U-Net
## 7 Conclusions  
Intracranial Hemorrhage (ICH) is a severe condition with extremely high rates of mortality. Identification and segmentation of CT scans of patients suspected of suffering ICH are vital to formulating treatment and surgery plans. Despite the importance of this issue, there are very few reliable solutions to ICH segmentation without a medical professional. Therefore, it is imperative to design accurate and robust methods to segment ICH areas from CT scans. With high enough accuracy rates, these models could potentially outperform trained professionals, leading to fewer false-negative ICH detections. Unfortunately, due to the cost and nature of acquiring expert-labeled CT scans of ICH patients, the repository for training data for newer deep learning algorithms is often not enough to produce robust segmentation models. In this paper, we proposed a modified u-net and curriculum learning strategy for the semi-supervised model initially introduced by [PERSON] et al. [34] to segment ICH regions from patient CT scans automatically. The adopted curriculum learning strategy solved the gradient explosion problem that was encountered during our experiments of [PERSON]'s alternate learning method. The central idea of this training procedure was to optimize mainly the supervised portion of the model at the beginning, then eventually converge to [PERSON]'s alternating learning algorithm. This new model worked with a small labeled dataset and a large unlabeled dataset. With our segmentation model, we trained and tested a purely supervised version and pretrained modified u-net and showed that our model surpassed both regardless of the amount of data used to train it. This being said, the Dice and Jaccard coefficients of our final solutions were still far from perfect. Due to the volatile nature of performing machine learning on small datasets, we could not guarantee the integrity of this algorithm when extended to different domains and datasets. While we could  
Figure 3: Ground truth and prediction pulled from our segmentation model (semi-supervised trained on 80% of our data). The corresponding Dice (Jaccard) coefficient values are also given. Purple regions indicate intracranial hemorrhages. Please be advised that optimizing either Dice or Jaccard coefficients produced almost identical segmentation results visually; therefore, only one predicted image for each example is shown. empirically show that our algorithm had an improved stability compared to the original method proposed by [PERSON] et al. [34], more work and experimentation need to be done with a variety of tasks and datasets to confirm the reliability of its results. In the future research, we plan to study ways to improve the accuracy and robustness of the segmentation model. As seen in Figure 3, one issue that our model encountered was the inability to segment smaller regions while optimizing larger ones. This is a common problem in semantic segmentation and is mainly due to class imbalance. A future direction is to integrate the semi-supervised model with other loss functions, say Tversky loss [47], to combat class imbalance. Conceptualization, J.L.W. ; methodology, J.L.W. ; software, J.L.W. ; validation, J.L.W. ; formal analysis, J.L.W. ; investigation, J.L.W. ; resources, H.Z. ; data curation, A.K.I. ; writing, original draft preparation, J.L.W. and H.F.; writing, review and editing, J.L.W., H.F., and H.Z. ; visualization, J.L.W. ; supervision., H.Z. ; project administration, H.Z. All authors read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: