A path along deep learning for medical image analysis : With focus on burn wounds and brain tumors

Abstract: The number of medical images that clinicians need to review on a daily basis has increased dramatically during the last decades. Since the number of clinicians has not increased as much, it is necessary to develop tools which can help doctors to work more efficiently. Deep learning is the last trend in the medical imaging field, as methods based on deep learning often outperform more traditional analysis methods. However, in medical imaging a general problem for deep learning is to obtain large, annotated datasets for training the deep networks.This thesis presents how deep learning can be used for two medical problems: assessment of burn wounds and brain tumors. The first papers present methods for analyzing 2D burn wound images; to estimate how large the burn wound is (through image segmentation) and to classify how deep a burn wound is (image classification). The last papers present methods for analyzing 3D magnetic resonance imaging (MRI) volumes containing brain tumors; to estimate how large the different parts of the tumor are (image segmentation). Since medical imaging datasets are often rather small, image augmentation is necessary to artificially increase the size of the dataset and, at the same time, the performance of a convolutional neural network. Traditional augmentation techniques simply apply operations such as rotation, scaling and elastic deformations to generate new similar images, but it is often not clear what type of augmentation that is best for a certain problem. Generative adversarial networks (GANs), on the other hand, can generate completely new images by learning the high dimensional data distribution of images and sampling from it (which can be seen as advanced augmentation). GANs can also be trained to generate images of type B from images of type A, which can be used for image segmentation.  The conclusion of this thesis is that deep learning is a powerful technology that doctors can benefit from, to assess injuries and diseases more accurately and more quickly. In the end, this can lead to better healthcare for the patients.