# Segmentation of Intracranial Hemorrhage Using Semi-Supervised Multi-Task Attention-Based U-Net
## 1 Introduction  
The main risk factors of Intracranial Hemorrhage (ICH), which have extremely high rates of mortality, include hypertension and cerebral amyloid angiopathy. Other risk factors include alcohol intake, low levels of cholesterol, the \(\epsilon 2\) and \(\epsilon 4\) alleles of the Apolipoprotein E gene, anticoagulation treatment, and drug abuse. A depressing fact is that ICH occurs twice as frequently in low-/middle-income countries as in high-income countries [1]. Traditionally, ICH can be diagnosed from inspecting CT scans of the patient by a medical specialist. To save the time and effort of medical specialists, automated detection of ICH from CT scans becomes important. In addition, such a practice can improve the ICH diagnosis rate, especially in areas with limited access to medical professionals, potentially saving countless lives [2, 3]. Deep learning has been applied to medical images for interpretation tasks with promising results [4]. For instance, [PERSON] et al. [5] created a curriculum-style strategy for a semi-supervised CNN designed for segmentation tasks based on inequality constraints. This model was tested on left ventricle segmentation using MRI scans. However, it was unable to outperform a constrained CNN, which was pointed out in [6], although it was within a 1-3% margin when the number of labeled patients was increased to 40. Deep learning algorithms have further been used to find abnormalities in CT images of head [7] and chest [8], among others, allowing us to automate such tasks. Recently,deep learning has found applications in hemorrhage detection. Many researchers have focused on solving this problem by either directly detecting ICH in general or a specific sub-group of ICH in a given image [9; 10; 11]. Most have used small datasets simply due to the limited availability of medical image data [12; 13; 14]. As regards the ICH sub-groups, they were classified as follows: Intraventricular (IVH), Intraparenchymal (IPH), Subarachnoid (SAH), Epidural (EDH), and Subdural (SDH). Others have worked on segmentation in order to highlight the specific regions where the ICH lies, assuming one was present [15; 16]. [PERSON] et al. [17] used basic pattern recognition techniques in conjunction with a threshold-based algorithm in order to detect ICH, demonstrating 98% sensitivity with 59% specificity for ICH detection. [PERSON] et al. [18] proposed a hemorrhage detection algorithm using a variant of distance regularized level set evolution along with shape and texture features to detect and extract these regions. This method was deemed to work well with certain hemorrhage types, such as EDH, where obvious borders exist, in which case it was able to achieve a similarity rate above 75%. However, it preformed quite poorly with other types of hemorrhage, such as SDH, where it achieved below a 40% similarity rate. [PERSON] et al. [19] created a fully convolutional neural network based on the PatchFCN model [20] and trained on a dataset of approximately 4400 CT scans. Their model was then run on 200 test images, with the results being compared to those of four American Board of Radiology certified radiologists. It was observed that their model beat two of the said radiologists. These results were based on a binary decision as to whether or not an ICH was present in a given image with a segmentation task not in consideration. Another paper by [PERSON] et al. [4] introduced four algorithms for detecting sub-types of ICH trained on a large dataset containing nearly 300,000 CT scans. The average sensitivity was 92%, but the average specificity fell short at only 68% [17]. Much more recently, a paper by [PERSON] et al. detailed the use of a conventional u-net to detect ICH regions in CT scans. Their low Dice coefficient of 0.31 based on a five-fold cross-validation left room for improvement [2]. [PERSON] et al. [21] introduced affinity graphs, an undirected weighted graph representing pixel connectivity, where classes with each affinity were defined with a segmentation mask and indicator function.