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The previous decade has seen breakthroughs in image analysis and computer vision, mainly due to machine learning methods known as deep learning. These methods have since spread to other fields. This thesis aims to survey the progress, highlight problems related to data and computations, and show techniques to mitigate them.

In Paper I, we show how to modify the VGG16 classifier architecture to be equivariant to transformations in the p4 group, consisting of translations and specific rotations. We conduct experiments to investigate if baseline architectures, using data augmentation, can be replaced with these rotation-equivariant networks. We train and test on the Oral cancer dataset, used to automate cancer diagnostics.

In Paper III, we use a similar methodology as in Paper I to modify the U-net architecture combined with a discriminative loss, for semantic instance segmentation. We test the method on the BBBC038 dataset consisting of highly varied images of cell nuclei.

In Paper II, we look at the UCluster method, used to group sub- atomic particles in particle physics. We show how to distribute the training over multiple GPUs using distributed deep learning in a cloud environment.

The papers show how to use limited training data more efficiently, using group-equivariant convolutions, to reduce the prob- lems of overfitting. They also demonstrate how to distribute training over multiple nodes in computational centers, which is needed to handle growing data sizes.