Understanding the dynamics of blood flow is crucial in the context of cardiovascular health and disease. The dynamics of the blood flow can be a significant parameter for the development of decision support systems to enable early detection and accurate diagnosis of coronary artery diseases. Uncovering the underlying dynamics from high-dimensional data generated from experiments is a highly complex problem at the intersection of artificial intelligence and applied mathematics. Deep Hidden Physics Models can be used to learn the underlying dynamics without additional physical knowledge.In this work, the potential of Deep Hidden Physics Models to model the clinically relevant dynamics of blood flow is investigated. The experiments consider the use case of stenosis in two-dimensional spatial space. Based on the learned dynamics, the concentration field can be approximated accurately, indicating that the dynamics are learned correctly. Additionally, we examine the capability of the model to extrapolate the learned dynamics for unknown time intervals.

Deep Learning is proving its immense potential in medical image processing. However, noisy label data can weaken the generalization ability of the model and cause significant performance degradation. This issue can be more prevalent in multi-label classification tasks, since their annotation is more challenging. With too many labels, human annotators may have difficulties mentioning all possible classes, which leads to an increased number of false negative labels. Single Positive Multi-Label (SPML) training deals with the most severe version of this problem, in which for each sample only one positive label is available. All other labels are not observed, i.e. not confirmed as positive or negative. While SPML has already achieved good results for the multi-label detection of objects, its impact on the extremely pertinent medical imaging use case has not yet been explored. In this work, we therefore investigate the performance of state-of-the-art SPML loss functions in the analysis of chest X-rays, both on the public CheXpert and on an in-house data set of the University Hospital Bonn. In addition, we propose our new SPML loss functions, the Generalized Assume Negative and Implicit Weighting Assume Negative loss, which increase the mean average precision by up to 4.6% compared to the popular binary cross-entropy loss.

We present the Automated List Inspection (ALI) tool that utilizes methods from machine learning, natural language processing, combined with domain expert knowledge to automate financial statement auditing. ALI is a content based context-aware recommender system, that matches relevant text passages from the notes to the financial statement to specific law regulations. In this paper, we present the architecture of the recommender tool which includes text mining, language modeling, unsupervised and supervised methods that range from binary classification models to deep recurrent neural networks. Next to our main findings, we present quantitative and qualitative comparisons of the algorithms as well as concepts for how to further extend the functionality of the tool.