The usage of deep learning models for tagging input data has increased over the past years because of their accuracy and high performance. 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.
|Tijdschrift||Computer Graphics Forum|
|Nummer van het tijdschrift||3|
|Status||Gepubliceerd - 21 mrt 2019|
|Evenement||21st Eurographics/IEEE VGTC Conference on Visualization - Alfandega do Porto Congress Centre, Porto, Portugal|
Duur: 3 jun 2019 → 7 jun 2019