@article{Gillmann-2021-Viewpoints,
author = {C. Gillmann and N. N. Smit and E. Groller and B. Preim and A. Vilanova and T. Wischgoll},
journal = {IEEE Computer Graphics and Applications},
title = {Ten Open Challenges in Medical Visualization},
year = {2021},
volume = {41},
number = {05},
issn = {1558-1756},
pages = {7-15},
keywords = {deep learning;uncertainty;data visualization;medical services;standardization;artificial intelligence;biomedical imaging},
doi = {10.1109/MCG.2021.3094858},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
pdf = {pdfs/Gillmann-2021-Viewpoints.pdf},
thumbnails = {images/Gillmann-2021-Viewpoints.png},
images = {images/Gillmann-2021-Viewpoints.png},
project = {ttmedvis},
abstract = {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.}
}