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dc.contributor.authorGarcia Caballero, Humbertoen_US
dc.contributor.authorWestenberg, Michelen_US
dc.contributor.authorGebre, Binyamen_US
dc.contributor.authorWijk, Jarke J. vanen_US
dc.contributor.editorGleicher, Michael and Viola, Ivan and Leitte, Heikeen_US
dc.description.abstractThe usage of deep learning models for tagging input data has increased over the past years because of their accuracy and highperformance. A successful application is to score sleep stages. In this scenario, models are trained to predict the sleep stages of individuals. Although their predictive accuracy is high, there are still misclassifications that prevent doctors from properly diagnosing sleep-related disorders. This paper presents a system that allows users to explore the output of deep learning models in a real-life scenario to spot and analyze faulty predictions. These can be corrected by users to generate a sequence of sleep stages to be examined by doctors. Our approach addresses a real-life scenario with absence of ground truth. It differs from others in that our goal is not to improve the model itself, but to correct the predictions it provides. We demonstrate that our approach is effective in identifying faulty predictions and helping users to fix them in the proposed use case.en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectcentered computing
dc.subjectVisual analytics
dc.titleV-Awake: A Visual Analytics Approach for Correcting Sleep Predictions from Deep Learning Modelsen_US
dc.description.seriesinformationComputer Graphics Forum
dc.description.sectionheadersBest Paper Award Nominees

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  • 38-Issue 3
    EuroVis 2019 - Conference Proceedings

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