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In the past ten years, with the help of deep learning, especially the rapiddevelopment of deep neural networks, medical image analysis has made remarkableprogress. However, how to effectively use the relational information betweenvarious tissues or organs in medical images is still a very challengingproblem, and it has not been fully studied. In this thesis, we propose twonovel solutions to this problem based on deep relational learning. First, wepropose a context-aware fully convolutional network that effectively modelsimplicit relation information between features to perform medical imagesegmentation. The network achieves the state-of-the-art segmentation results onthe Multi Modal Brain Tumor Segmentation 2017 (BraTS2017) and Multi Modal BrainTumor Segmentation 2018 (BraTS2018) data sets. Subsequently, we propose a newhierarchical homography estimation network to achieve accurate medical imagemosaicing by learning the explicit spatial relationship between adjacentframes. We use the UCL Fetoscopy Placenta dataset to conduct experiments andour hierarchical homography estimation network outperforms the otherstate-of-the-art mosaicing methods while generating robust and meaningfulmosaicing result on unseen frames.

Medical Image Analysis using Deep Relational Learning