Representation Learning and Information Fusion : Applications in Biomedical Image Processing

Sammanfattning: In recent years Machine Learning and in particular Deep Learning have excelled in object recognition and classification tasks in computer vision. As these methods extract features from the data itself by learning features that are relevant for a particular task, a key aspect of this remarkable success is the amount of data on which these methods train. Biomedical applications face the problem that the amount of training data is limited. In particular, labels and annotations are usually scarce and expensive to obtain as they require biological or medical expertise. One way to overcome this issue is to use additional knowledge about the data at hand. This guidance can come from expert knowledge, which puts focus on specific, relevant characteristics in the images, or geometric priors which can be used to exploit the spatial relationships in the images. This thesis presents machine learning methods for visual data that exploit such additional information and build upon classic image processing techniques, to combine the strengths of both model- and learning-based approaches. The thesis comprises five papers with applications in digital pathology. Two of them study the use and fusion of texture features within convolutional neural networks for image classification tasks. The other three papers study rotational equivariant representation learning, and show that learned, shared representations of multimodal images can be used for multimodal image registration and cross-modality image retrieval.