# Multi-Task Learning for Small Brain Tumor Segmentation from MRI
## 1 Introduction
Although the U-Net uses the skip connection to restore the higher resolution features from the encoder part to decoder part, it often fails to keep the detailed information of the input or to retain the features of small objects [6]. Consequently, U-Net based models often miss out on small objects. Several deep learning methods have been proposed for dealing with the small objects problem. They are often based on multiresolution [7], multiple kernel sizes [8], or using dilated convolution [6] for capturing multiscale features. The multiresolution and multiple kernel sizes work well on 2D but they become very heavy and complex when dealing with 3D MRI data. The dilated convolution is good for capturing multiscale features without adding many weights. However, they are effective on rather large objects, not small objects, because of an enlarged receptive field. The dilated convolution is used in our network for multiscale tumor segmentation, however, we need an additional method for small tumors. In this paper, we try a new approach using multi-task learning with feature reconstruction, which is very effective for retaining the important features for small tumors [9]. Multi-task learning is a technique where the network optimizes two or more tasks at the same time. In addition to the main task, which provides training for the original purpose of the network, auxiliary tasks are used. The aim of the auxiliary task is to improve the model's convergence or optimization for the benefit of the main task. Those tasks are usually related and share common features. Multi-task learning has been used very successfully in many applications of deep learning such as image classification [10], video captioning [11], voice enhancement [12], or medical image segmentation [13; 14]. Based on the idea of an autoencoder, [PERSON] et al. [9] proposed U-module as another task for better initialization of the parameters of CNN for medical image classification. [PERSON] used CNN with U-module embedded in the early stage of the network, instead of putting it to the end of the network, for classification [9]. The U-module is added as an auxiliary task for feature reconstruction. It helps to better initialize parameters of the model and at the same time it retains the detailed features as much as possible. In this paper, we use multi-task learning with the auxiliary task of feature reconstruction to preserve detailed features for small tumors, which are often lost in down-sampled layers. We define small tumors as the group of tumors of size about less than 10% of the median. In the case of enhancing tumors, which is the main target of experiments, the median is about the size of 20,000 voxels, therefore the small tumors are defined to be of size less than 2000 voxels. The experiments are performed in the tumors of size less than 1000 voxels and less than 2000 voxels each. Specifically, we devised a new multi-task learning network based on U-Net, where U-module is embedded in the encoding stage of the network. Our network is simple and easy to run in medium hardware specification and has been successful in dealing with small tumors. To the best of our knowledge, this is the first time multi-task learning is used to detect and segment small objects. Even though there have been approaches [13; 14] using multi-task learning for medical image segmentation, they have not been targeted for hierarchical analysis. The contribution of the paper can be summarized as follows:  
1. Most of the previous studies seldom considered the small-sized tumors, which have a significant impact on finding early-stage cancers. This paper focuses on solving the problem of accurately identifying multiscale tumors, especially very small-sized tumors in the brain. 2. Instead of using heavyweight networks with multiresolution or multiple kernel sizes, we propose a novel approach for better segmentation of small tumors by dilated convolution and multi-task learning.