End-to-End Learning of Deep Structured Models for Semantic Segmentation

The task of semantic segmentation aims at understanding an image at a pixel level. This means assigning a label to each pixel of an image, describing the object it is depicting. Due to its applicability in many areas, such as autonomous vehicles, robotics and medical surgery assistance, semantic segmentation has become an essential task in image analysis. During the last few years a lot of progress have been made for image segmentation algorithms, mainly due to the introduction of deep learning methods, in particular the use of Convolutional Neural Networks (CNNs). CNNs are powerful for modeling complex connections between input and output data but lack the ability to directly model dependent output structures, for instance, enforcing properties such as label smoothness and coherence. This drawback motivates the use of Conditional Random Fields (CRFs), widely applied as a post-processing step in semantic segmentation.This thesis summarizes the content of three papers, all of them presenting solutions to semantic segmentation problems. The applications have varied widely and several different types of data have been considered, ranging from 3D CT images to RGB images of horses. The main focus has been on developing robust and accurate models to solve these problems. The models consist of a CNN capable of learning complex image features coupled with a CRF capable of learning dependencies between output variables. Emphasis has been on creating models that are possible to train end-to-end, as well as developing corresponding optimization methods needed to enable efficient training. End-to-end training gives the CNN and the CRF a chance to learn how to interact and exploit complementary information to achieve better performance

Platform to Assist Medical Experts in Training, Application, and Control of Machine Learning Models Using Patient Data from a Clinical Information System

In recent years, clinical data scientists achieved major breakthroughs advancing machine learning models for the medical domain, which have great potential assisting medical experts. Machine learning models can be leveraged to assist medical experts in tasks such as analyzing and diagnosing patient data, for example, from computed tomography scans. However, it is a challenge to translate the latest advancements in academic research fields such as computer sciences and physics into clinical practice. For this purpose, clinical data scientists and medical experts need to closely collaborate. This thesis tackles challenges of accessibility and usability of state-of-the-art machine learning models as well as designing a scalable computing architecture. Hence, conceptual ideas of possible strategies, as well as a prototype of such a machine learning platform, are presented. A systematic literature review was conducted on the current approaches to create medical machine learning platforms, the management of machine learning models, and the version management of large data sets. Afterward, the functional and nonfunctional requirements of the new machine learning platform were elicited as part of the requirements analysis. Two streamlined workflows for clinical data scientists and medical experts were derived from the requirement analysis. The workflow for the clinical data scientists includes steps to define, train, and share machine learning methods, including pre- and postprocessing modules, and management of data sets. Medical experts are able to analyze patient data using pre-defined machine learning methods. Building on the result of these analyses, the architecture of the platform was derived. The architecture consists of a scalable infrastructure stack, a lightweight and easy-to-use web interface, as well as a backend component to provide the required functionalities. The final design decisions solve the issue of efficiently standardizing, parallelizing, and applying machine learning workflows within a scalable computing infrastructure. The proposed platform was evaluated with 22 participants, consisting of clinical data scientists (N=12) and medical experts (N=10). Both groups were asked to rate specific workflows of the platform, as well as the platform as a whole, and to provide additional ideas and feedback. 92% of the medical experts and 90% of the clinical data scientists rated their overall impression of the platform as very good. Furthermore, medical experts and clinical data scientists strongly agreed that the platform facilitates method development and collaborations with 92% and 90%, respectively. The conducted expert survey suggests that the here proposed platform could be used to develop, optimize, and apply machine learning methods in the medical domain and beyond, thereby easing the collaboration between medical experts and clinical data scientists