  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.
