Blar i NTNU Open på forfatter "Storås, Andrea"
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Bias og kvantitativ analyse innen velferd: opphav til skjevheter og relasjon til utfallsrettferdighet
Storås, Andrea; Prabhu, Robindra; Hammer, Hugo Lewi; Strumke, Inga (Peer reviewed; Journal article, 2022)Ifølge Norges nasjonale strategi for kunstig intelligens er offentlig forvaltning og helse blant Norges satsingsområder for bruk av kunstig intelligens. Maskinlæring er en undergruppe av kunstig intelligens med potensial ... -
Experiences and Lessons Learned from a Crowdsourced-Remote Hybrid User Survey Framework
Midoglu, Cise; Storås, Andrea; Sabet, Saeed; Hammou, Malek; Hicks, Steven; Strumke, Inga; Riegler, Michael; Griwodz, Carsten; Halvorsen, Pål (Chapter, 2022)Subjective user studies are important to ensure the fidelity and usability of systems that generate multimedia content. Testing how end-users and domain experts perceive multimedia assets might provide crucial information. ... -
Predicting an unstable tear film through artificial intelligence
Fineide, Fredrik; Storås, Andrea; Chen, Xiangjun; Magnø, Morten Schjerven; Yazidi, Anis; Riegler, Michael; Utheim, Tor Paaske (Peer reviewed; Journal article, 2022)Dry eye disease is one of the most common ophthalmological complaints and is defined by a loss of tear film homeostasis. Establishing a diagnosis can be time-consuming, resource demanding and unpleasant for the patient. ... -
Predicting Tacrolimus Exposure in Kidney Transplanted Patients Using Machine Learning
Storås, Andrea; Åsberg, Anders; Halvorsen, Pål; Riegler, Michael Alexander; Strumke, Inga (Chapter, 2022) -
Research proposal: Explainability methods for machine learning systems for multimodal medical datasets
Storås, Andrea; Strumke, Inga; Riegler, Michael Alexander; Halvorsen, Pål (Chapter, 2022)This paper contains the research proposal of Andrea M. Storås that was presented at the MMSys 2022 doctoral symposium. Machine learning models have the ability to solve medical tasks with a high level of performance, e.g., ... -
Usefulness of Heat Map Explanations for Deep-Learning-Based Electrocardiogram Analysis
Storås, Andrea; Andersen, Ole Emil; Lockhart, Sam; Thielemann, Roman; Gnesin, Filip; Thambawita, Vajira L B; Hicks, Steven; Kanters, Jørgen K.; Strumke, Inga; Halvorsen, Pål; Riegler, Michael (Peer reviewed; Journal article, 2023)Deep neural networks are complex machine learning models that have shown promising results in analyzing high-dimensional data such as those collected from medical examinations. Such models have the potential to provide ... -
Using machine learning model explanations to identify proteins related to severity of meibomian gland dysfunction
Storås, Andrea; Fineide, Fredrik; Magnø, Morten Schjerven; Thiede, Bernd; Chen, Xiangjun; Strumke, Inga; Halvorsen, Pål; Galtung, Hilde; Jensen, Janicke Cecilie Liaaen; Utheim, Tor Paaske; Riegler, Michael Alexander (Peer reviewed; Journal article, 2023)Meibomian gland dysfunction is the most common cause of dry eye disease and leads to significantly reduced quality of life and social burdens. Because meibomian gland dysfunction results in impaired function of the tear ... -
Using machine learning model explanations to identify proteins related to severity of meibomian gland dysfunction
Storås, Andrea; Fineide, Fredrik; Magnø, Morten Schjerven; Thiede, Bernd; Chen, Xiangjun; Strumke, Inga; Halvorsen, Pål; Galtung, Hilde; Jensen, Janicke Cecilie Liaaen; Utheim, Tor Paaske; Riegler, Michael Alexander (Peer reviewed; Journal article, 2023)Meibomian gland dysfunction is the most common cause of dry eye disease and leads to significantly reduced quality of life and social burdens. Because meibomian gland dysfunction results in impaired function of the tear ... -
Visual explanations for polyp detection: How medical doctors assess intrinsic versus extrinsic explanations
Hicks, Steven; Storås, Andrea; Riegler, Michael; Midoglu, Cise; Hammou, Malek; Lange, Thomas de; Parasa, Sravanthi; Halvorsen, Pål; Strumke, Inga (Journal article; Peer reviewed, 2024)Deep learning has achieved immense success in computer vision and has the potential to help physicians analyze visual content for disease and other abnormalities. However, the current state of deep learning is very much a ...