Visual Analytics in Digital Pathology: Challenges and Opportunities
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Date
2019
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
The advances in high-throughput digitization, digital pathology systems, and quantitative image analysis opened new horizons in pathology. The diagnostic work of the pathologists and their role is likely to be augmented with computer-assistance and more quantitative information at hand. The recent success of artificial intelligence (AI) and computer vision methods demonstrated that in the coming years machines will support pathologists in typically tedious and highly subjective tasks and also in better patient stratification. In spite of clear future improvements in the diagnostic workflow, questions on how to effectively support the pathologists and how to integrate current data sources and quantitative information still persist. In this context, Visual Analytics (VA) - as the discipline that aids users to solve complex problems with an interactive and visual approach - can play a vital role to support the cognitive skills of pathologists and the large volumes of data available. To identify the main opportunities to employ VA in digital pathology systems, we conducted a survey with 20 pathologists to characterize the diagnostic practice and needs from a user perspective. From our findings, we discuss how VA can leverage quantitative image data to empower pathologists with new advanced digital pathology systems.
Description
@inproceedings{10.2312:vcbm.20191240,
booktitle = {Eurographics Workshop on Visual Computing for Biology and Medicine},
editor = {Kozlíková, Barbora and Linsen, Lars and Vázquez, Pere-Pau and Lawonn, Kai and Raidou, Renata Georgia},
title = {{Visual Analytics in Digital Pathology: Challenges and Opportunities}},
author = {Corvò, Alberto and Westenberg, Michel A. and Wimberger-Friedl, Reinhold and Fromme, Stephan and Peeters, Michel M. R. and Driel, Marc A. van and Wijk, Jarke J. van},
year = {2019},
publisher = {The Eurographics Association},
ISSN = {2070-5786},
ISBN = {978-3-03868-081-9},
DOI = {10.2312/vcbm.20191240}
}