# Multi-Task Learning for Small Brain Tumor Segmentation from MRI
## 1 Introduction  
Among cancers originally from the brain, glioma is one of the most common frequent [1]. Glioma arises from glial cells and can be separated into low-grade glioma and high-grade glioma. The high-grade gliomas are malignant and they often have a mean survival time of 15 months [2]. Low-grade gliomas may be malignant or benign. They develop slowly and can be progressed to high-grade gliomas. Currently, the most common treatment methods for gliomas are radiotherapy, chemotherapy, and surgery. Of all the brain tumor treatments, it is important for segmenting the tumors and their surrounding tissues accurately because it helps doctors to evaluate disease progression, treatment response, and therapy planning for glioma patients. Computed tomography (CT), Positron emission tomography (PET), and magnetic resonance imaging (MRI) are the most common ways of detecting and monitoring tumors. Among these ways, MRI is the first chosen method because it has a high resolution, superior contrast, and no harm to patients' health. In the conventional method of clinical diagnosis and treatment, the doctor needs to navigate 3D images search and segment tumor areas manually, which is very boring, time-consuming, and requires high expertise. Due to thosereasons, the automatic segmentation of brain tumors is critical in reducing the workload of doctors and has a significant impact on improving the diagnostic process, survival prediction, and treatment planning of patients with gliomas. Unlike some types of brain tumors, such as meningioma, which are easy to localize and segment, other tumors like glioblastomas and gliomas are much more troublesome to delineate. Glioma tumors and neighboring edema are dispersed, low-contrast, and appearing in many forms that make them hard for segmentation. Another essential trouble with locating brain tumors is that they can occur everywhere within brain areas, in nearly any shape and size. In addition, the scale and range of each voxel values in MRI is not uniform like other images obtained from CT scans or X-ray. Several factors affect image voxel values such as the MR machine type to be used and different acquisition protocols. Consequently, the same tumor cells may have significantly different grayscale values when being taken at different hospitals or institutes. Recently, various methods and networks, particularly ones based on deep learning with convolutional neural network (CNN), have been developed to automatically identify and segment brain tumors in medical images. Most models proposed for brain tumor segmentation focus on increasing overall performance. Several studies have worked on the problem in detection and segmentation of small-sized tumors. The tumor size is a very important factor. If their size is big, the survival rate will be low. As a consequence, it is important to detect brain tumors when they are still small. However, it is very tough to detect small brain tumors with human eyes [3]. [PERSON] et al. [4] proposed a segmentation model for detecting early-stage of brain tumors. The model was developed to segment small, medium, and large size tumors. However, the model was built based on traditional methods and did not achieve high efficiency. [PERSON] et al. [3] proposed a network to segment brain tumors at their very early-stages. The network utilized k-means clustering, object counting, patch-based technique, and tumor evaluation. The network can segment the tumor area with various sizes, especially for very small-sized tumors. However, the method was evaluated and implemented on a small amount of data with only twenty brain MRI scans. Most recent brain tumor segmentation methods using deep learning are based on U-Net [5]. However, U-Net architecture uses of the down-sampling layer to reduce feature resolution. The resolution plays a very important role in detecting and segmenting small objects.