On evaluation metrics for medical applications of artificial intelligence SA Hicks, I Strümke, V Thambawita, M Hammou, MA Riegler, P Halvorsen, ... Scientific reports 12 (1), 5979, 2022 | 287 | 2022 |
Science with e-ASTROGAM: A space mission for MeV–GeV gamma-ray astrophysics A De Angelis, V Tatischeff, IA Grenier, J McEnery, M Mallamaci, M Tavani, ... Journal of High Energy Astrophysics 19, 1-106, 2018 | 216 | 2018 |
Shapley values for feature selection: The good, the bad, and the axioms D Fryer, I Strümke, H Nguyen Ieee Access 9, 144352-144360, 2021 | 165 | 2021 |
Impact of image resolution on deep learning performance in endoscopy image classification: An experimental study using a large dataset of endoscopic images V Thambawita, I Strümke, SA Hicks, P Halvorsen, S Parasa, MA Riegler Diagnostics 11 (12), 2183, 2021 | 72 | 2021 |
To explain or not to explain?—Artificial intelligence explainability in clinical decision support systems J Amann, D Vetter, SN Blomberg, HC Christensen, M Coffee, S Gerke, ... PLOS Digital Health 1 (2), e0000016, 2022 | 71 | 2022 |
Explaining deep neural networks for knowledge discovery in electrocardiogram analysis SA Hicks, JL Isaksen, V Thambawita, J Ghouse, G Ahlberg, A Linneberg, ... Scientific reports 11 (1), 10949, 2021 | 58 | 2021 |
DeepFake electrocardiograms using generative adversarial networks are the beginning of the end for privacy issues in medicine V Thambawita, JL Isaksen, SA Hicks, J Ghouse, G Ahlberg, A Linneberg, ... Scientific reports 11 (1), 21896, 2021 | 46 | 2021 |
Artificial intelligence in dry eye disease AM Storås, I Strümke, MA Riegler, J Grauslund, HL Hammer, A Yazidi, ... The ocular surface 23, 74-86, 2022 | 39 | 2022 |
Lessons on interpretable machine learning from particle physics C Grojean, A Paul, Z Qian, I Strümke Nature Reviews Physics 4 (5), 284-286, 2022 | 27 | 2022 |
The social dilemma in artificial intelligence development and why we have to solve it I Strümke, M Slavkovik, VI Madai AI and Ethics 2 (4), 655-665, 2022 | 20 | 2022 |
Explaining a deep reinforcement learning docking agent using linear model trees with user adapted visualization VB Gjærum, I Strümke, OA Alsos, AM Lekkas Journal of Marine Science and Engineering 9 (11), 1178, 2021 | 17 | 2021 |
Model tree methods for explaining deep reinforcement learning agents in real-time robotic applications VB Gjærum, I Strümke, J Løver, T Miller, AM Lekkas Neurocomputing 515, 133-144, 2023 | 15 | 2023 |
Beyond cuts in small signal scenarios: Enhanced sneutrino detectability using machine learning D Alvestad, N Fomin, J Kersten, S Maeland, I Strümke The European Physical Journal C 83 (5), 379, 2023 | 12 | 2023 |
Trilinear-augmented gaugino mediation J Heisig, J Kersten, N Murphy, I Strümke Journal of High Energy Physics 2017 (5), 1-21, 2017 | 12 | 2017 |
Approximating a deep reinforcement learning docking agent using linear model trees VB Gjærum, ELH Rørvik, AM Lekkas 2021 European Control Conference (ECC), 1465-1471, 2021 | 9 | 2021 |
Model independent feature attributions: Shapley values that uncover non-linear dependencies DV Fryer, I Strumke, N Hien PeerJ Computer Science 7 (e582), 2021 | 9 | 2021 |
Signal mixture estimation for degenerate heavy Higgses using a deep neural network A Kvellestad, S Maeland, I Strümke The European Physical Journal C 78 (12), 1-11, 2018 | 9 | 2018 |
Huldra: A framework for collecting crowdsourced feedback on multimedia assets M Hammou, C Midoglu, SA Hicks, A Storås, SS Sabet, I Strümke, ... Proceedings of the 13th ACM Multimedia Systems Conference, 203-209, 2022 | 6 | 2022 |
Causal versus marginal Shapley values for robotic lever manipulation controlled using deep reinforcement learning SB Remman, I Strümke, AM Lekkas 2022 American Control Conference (ACC), 2683-2690, 2022 | 6 | 2022 |
Explaining the data or explaining a model? Shapley values that uncover non-linear dependencies DV Fryer, I Strümke, H Nguyen arXiv preprint arXiv:2007.06011, 2020 | 6 | 2020 |