Follow
Inga Strümke
Title
Cited by
Cited by
Year
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
2242022
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
2132018
Shapley values for feature selection: The good, the bad, and the axioms
D Fryer, I Strümke, H Nguyen
Ieee Access 9, 144352-144360, 2021
1402021
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
652021
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
592022
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
552021
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
412021
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
312022
Lessons on interpretable machine learning from particle physics
C Grojean, A Paul, Z Qian, I Strümke
Nature Reviews Physics 4 (5), 284-286, 2022
252022
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
162022
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
162021
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
112023
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
102018
Trilinear-augmented gaugino mediation
J Heisig, J Kersten, N Murphy, I Strümke
Journal of High Energy Physics 2017 (5), 1-21, 2017
102017
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
82021
Model independent feature attributions: Shapley values that uncover non-linear dependencies
DV Fryer, I Strumke, N Hien
PeerJ Computer Science 7 (e582), 2021
82021
Beyond Cuts in Small Signal Scenarios--Enhanced Sneutrino Detectability Using Machine Learning
D Alvestad, N Fomin, J Kersten, S Maeland, I Strümke
arXiv preprint arXiv:2108.03125, 2021
72021
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
72020
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
62022
Causal connections between socioeconomic disparities and COVID-19 in the USA
T Banerjee, A Paul, V Srikanth, I Strümke
Scientific Reports 12 (1), 15827, 2022
52022
The system can't perform the operation now. Try again later.
Articles 1–20