In the past ten years, with the help of deep learning, especially the rapid development of deep neural networks, medical image analysis has made remarkable progress. However, how to effectively use the relational information between various tissues or organs in medical images is still a very challenging problem, and it has not been fully studied. In this thesis, we propose two novel solutions to this problem based on deep relational learning. First, we propose a context-aware fully convolutional network that effectively models implicit relation information between features to perform medical image segmentation. The network achieves the state-of-the-art segmentation results on the Multi Modal Brain Tumor Segmentation 2017 (BraTS2017) and Multi Modal Brain Tumor Segmentation 2018 (BraTS2018) data sets. Subsequently, we propose a new hierarchical homography estimation network to achieve accurate medical image mosaicing by learning the explicit spatial relationship between adjacent frames. We use the UCL Fetoscopy Placenta dataset to conduct experiments and our hierarchical homography estimation network outperforms the other state-of-the-art mosaicing methods while generating robust and meaningful mosaicing result on unseen frames.

翻译：在过去的十年中，借助深度学习（尤其是深度神经网络的快速发展），医学图像分析取得了显著进展。然而，如何有效利用医学图像中各组织或器官之间的关联信息仍然是一个极具挑战的问题，且尚未得到充分研究。本文针对该问题提出了两种基于深度关系学习的新颖解决方案。首先，我们提出了一种上下文感知的全卷积网络，通过有效建模特征间的隐式关联信息来实现医学图像分割。该网络在多模态脑肿瘤分割2017（BraTS2017）和多模态脑肿瘤分割2018（BraTS2018）数据集上取得了最先进的分割结果。随后，我们提出了一种新的分层单应性估计网络，通过学习相邻帧之间的显式空间关系来实现精确的医学图像拼接。我们使用UCL胎儿镜胎盘数据集进行实验，该分层单应性估计网络优于其他最先进的拼接方法，并在未见过的帧上生成了稳健且有意义的拼接结果。