Computational modeling of human reasoning processes for interpretable visual knowledge: a case study with radiographers

Abstract Visual reasoning is critical in many complex visual tasks in medicine such as radiology or pathology. It is challenging to explicitly explain reasoning processes due to the dynamic nature of real-time human cognition. A deeper understanding of such reasoning processes is necessary for impro...

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Autores principales:	Yu Li, Hongfei Cao, Carla M. Allen, Xin Wang, Sanda Erdelez, Chi-Ren Shyu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/bcddfbfd54a34ea9bc59b68f55c1976f
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id	oai:doaj.org-article:bcddfbfd54a34ea9bc59b68f55c1976f
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spelling	oai:doaj.org-article:bcddfbfd54a34ea9bc59b68f55c1976f2021-12-02T12:33:15ZComputational modeling of human reasoning processes for interpretable visual knowledge: a case study with radiographers10.1038/s41598-020-77550-92045-2322https://doaj.org/article/bcddfbfd54a34ea9bc59b68f55c1976f2020-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-77550-9https://doaj.org/toc/2045-2322Abstract Visual reasoning is critical in many complex visual tasks in medicine such as radiology or pathology. It is challenging to explicitly explain reasoning processes due to the dynamic nature of real-time human cognition. A deeper understanding of such reasoning processes is necessary for improving diagnostic accuracy and computational tools. Most computational analysis methods for visual attention utilize black-box algorithms which lack explainability and are therefore limited in understanding the visual reasoning processes. In this paper, we propose a computational method to quantify and dissect visual reasoning. The method characterizes spatial and temporal features and identifies common and contrast visual reasoning patterns to extract significant gaze activities. The visual reasoning patterns are explainable and can be compared among different groups to discover strategy differences. Experiments with radiographers of varied levels of expertise on 10 levels of visual tasks were conducted. Our empirical observations show that the method can capture the temporal and spatial features of human visual attention and distinguish expertise level. The extracted patterns are further examined and interpreted to showcase key differences between expertise levels in the visual reasoning processes. By revealing task-related reasoning processes, this method demonstrates potential for explaining human visual understanding.Yu LiHongfei CaoCarla M. AllenXin WangSanda ErdelezChi-Ren ShyuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-11 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yu Li Hongfei Cao Carla M. Allen Xin Wang Sanda Erdelez Chi-Ren Shyu Computational modeling of human reasoning processes for interpretable visual knowledge: a case study with radiographers
description	Abstract Visual reasoning is critical in many complex visual tasks in medicine such as radiology or pathology. It is challenging to explicitly explain reasoning processes due to the dynamic nature of real-time human cognition. A deeper understanding of such reasoning processes is necessary for improving diagnostic accuracy and computational tools. Most computational analysis methods for visual attention utilize black-box algorithms which lack explainability and are therefore limited in understanding the visual reasoning processes. In this paper, we propose a computational method to quantify and dissect visual reasoning. The method characterizes spatial and temporal features and identifies common and contrast visual reasoning patterns to extract significant gaze activities. The visual reasoning patterns are explainable and can be compared among different groups to discover strategy differences. Experiments with radiographers of varied levels of expertise on 10 levels of visual tasks were conducted. Our empirical observations show that the method can capture the temporal and spatial features of human visual attention and distinguish expertise level. The extracted patterns are further examined and interpreted to showcase key differences between expertise levels in the visual reasoning processes. By revealing task-related reasoning processes, this method demonstrates potential for explaining human visual understanding.
format	article
author	Yu Li Hongfei Cao Carla M. Allen Xin Wang Sanda Erdelez Chi-Ren Shyu
author_facet	Yu Li Hongfei Cao Carla M. Allen Xin Wang Sanda Erdelez Chi-Ren Shyu
author_sort	Yu Li
title	Computational modeling of human reasoning processes for interpretable visual knowledge: a case study with radiographers
title_short	Computational modeling of human reasoning processes for interpretable visual knowledge: a case study with radiographers
title_full	Computational modeling of human reasoning processes for interpretable visual knowledge: a case study with radiographers
title_fullStr	Computational modeling of human reasoning processes for interpretable visual knowledge: a case study with radiographers
title_full_unstemmed	Computational modeling of human reasoning processes for interpretable visual knowledge: a case study with radiographers
title_sort	computational modeling of human reasoning processes for interpretable visual knowledge: a case study with radiographers
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/bcddfbfd54a34ea9bc59b68f55c1976f
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_version_	1718393881984237568

Computational modeling of human reasoning processes for interpretable visual knowledge: a case study with radiographers

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