PhD defence S. (Shuai) Chen

Summary:

Deep learning is a widely used tool in medical imaging, and it is especially effective in segmentation applications. However, the traditional deep learning methods for image segmentation can have problems in applications where they lack the ability to learn global information for accurate segmentation and the full annotations for medical imaging segmentation are scarcely available. This thesis provides solutions to these problems, based on methods for global and data-efficient learning. Developing methods that can learn and use more global information within images and labels can be beneficial if the learned information such as global spatial relationships between objects or the global structural information in labels help to improve the segmentation performance. Making better use of available unlabeled data may alleviate the data scarcity problem, which requires more data-efficient learning strategies.