Improving Provenance Data Interaction for Visual Storytelling in Medical Imaging Data Exploration

Effective collaborative work in diagnostic medical imaging is not trivial due to the large amounts of complex data involved,a (non-linear) workflow involving experts in different domains, and a lack of versatility in the current tools employed inhealthcare. In this paper, we aim to introduce how the integration of visual storytelling techniques together with provenancedata in the analytic systems used in medicine can compensate for these issues, by enhancing communication of results andreproducibility of findings through diagnostic provenance data. To this end, we illustrate how we can improve the interactionwith provenance data displayed in a graph in order to facilitate authoring and the creation process of visual data storie

Ten Open Challenges in Medical Visualization

The medical domain has been an inspiring application area in visualization research for many years already, but many open challenges remain. The driving forces of medical visualization research have been strengthened by novel developments, for example, in deep learning, the advent of affordable VR technology, and the need to provide medical visualizations for broader audiences. At IEEE VIS 2020, we hosted an Application Spotlight session to highlight recent medical visualization research topics. With this article, we provide the visualization community with ten such open challenges, primarily focused on challenges related to the visualization of medical imaging data. We first describe the unique nature of medical data in terms of data preparation, access, and standardization. Subsequently, we cover open visualization research challenges related to uncertainty, multimodal and multiscale approaches, and evaluation. Finally, we emphasize challenges related to users focusing on explainable AI, immersive visualization, P4 medicine, and narrative visualization.acceptedVersio