Inga Strümke

A. Storås, O. E. Andersen, S. Lockhart, R. Thielemann, F. Gnesin, V. Thambawita, S. Hicks, J. Kanters, I. Strümke, P. Halvorsen and M. Riegler

Usefulness of Heat Map Explanations for Deep-Learning-Based Electrocardiogram Analysis

Diagnostics

A. Storås, M. Magnø, F. Fineide, B. Thiede, X. Chen, I. Strümke, P. Halvorsen, T. Utheim and M. Riegler

Identifying Important Proteins in Meibomian Gland Dysfunction with Explainable Artificial Intelligence

IEEE International Symposium on Computer-Based Medical Systems (IEEE CBMS2023)

A. Storås, I. Strümke, M. Riegler, J. Grauslund, H. L. Hammer, A. Yazidi, P. Halvorsen, K. G. Gundersen, T. P. Utheim and C. J. Jackson

Artificial intelligence in dry eye disease

The Ocular Surface

A. Storås, R. Prabhu, H. L. Hammer and I. Strümke

Bias og kvantitativ analyse innen velferd

Tidsskrift for velferdsforskning

S. Hicks, I. Strümke, V. Thambawita, M. Hammou, M. Riegler, P. Halvorsen and S. Parasa

On evaluation metrics for medical applications of artificial intelligence

Scientific Reports

J. Amann, D. Vetter, S. N. Blomberg, H. C. Christensen, M. Coffee, S. Gerke, T. K. Gilbert, T. Hagendorff, S. Holm, M. Livne, A. Spezzatti, I. Strümke, R. V. Zicari and V. I. Madai

To explain or not to explain?—Artificial intelligence explainability in clinical decision support systems

PLOS Digital Health

C. Midoglu, A. Storås, S. S. Sabet, M. Hammou, S. Hicks, I. Strümke, M. Riegler, C. Griwodz and P. Halvorsen

Experiences and Lessons Learned from a Crowdsourced-Remote Hybrid User Survey Framework

2022 IEEE International Symposium on Multimedia (ISM)

A. Storås, I. Strümke, M. Riegler and P. Halvorsen

Explainability methods for machine learning systems for multimodal medical datasets: research proposal

ACM Multimedia Systems (MMSys) Conference

M. Hammou, C. Midoglu, S. Hicks, A. Storås, S. S. Sabet, I. Strümke, M. Riegler and P. Halvorsen

Huldra: A Framework for Collecting Crowdsourced Feedback on Multimedia Assets

Proceedings of the 13th ACM Multimedia Systems Conference (MMSys '22)

A. Storås, A. Åsberg, P. Halvorsen, M. Riegler and I. Strümke

Predicting Tacrolimus Exposure in Kidney Transplanted Patients Using Machine Learning

35th IEEE CBMS International Symposium on Computer-Based Medical Systems

A. Storås, M. Riegler, P. Halvorsen, A. Åsberg and I. Strümke

Predicting drug exposure in kidney transplanted patients using machine learning

A. Storås, I. Strümke, M. Riegler and P. Halvorsen

Explainable Artificial Intelligence in Medicine

Nordic AI Meet 2022

O. Lysne, I. Strümke and M. Riegler

Data samlet fra åpne kilder er ikke ufarlig

S. Hicks, A. Storås, M. Riegler, C. Midoglu, M. Hammou, T. de Lange, S. Parasa, P. Halvorsen and I. Strümke

Visual explanations for polyp detection: How medical doctors assess intrinsic versus extrinsic explanations

V. Thambawita, J. L. Isaksen, S. Hicks, J. Ghouse, G. Ahlberg, A. Linneberg, N. Grarup, C. Ellervik, M. S. Olesen, T. Hansen, C. Graff, N. Holstein-Rathlou, I. Strümke, H. L. Hammer, M. Maleckar, P. Halvorsen, M. Riegler and J. K. Kanters

DeepFake electrocardiograms using generative adversarial networks are the beginning of the end for privacy issues in medicine

Nature Scientific Reports

V. Thambawita, J. L. Isaksen, S. Hicks, J. Ghouse, G. Ahlberg, A. Linneberg, N. Grarup, C. Ellervik, M. S. Olesen, T. Hansen, C. Graff, N. Holstein-Rathlou, I. Strümke, H. L. Hammer, M. Maleckar, P. Halvorsen, M. Riegler and J. K. Kanters

DeepFake electrocardiograms: the key for open science for artificial intelligence in medicine

Scientific Reports

S. Hicks, J. L. Isaksen, V. Thambawita, J. Ghouse, G. Ahlberg, A. Linneberg, N. Grarup, I. Strümke, C. Ellervik, M. S. Olesen, T. Hansen, C. Graff, N. Holstein-Rathlou, P. Halvorsen, M. Maleckar, M. Riegler and J. K. Kanters

Explaining deep neural networks for knowledge discovery in electrocardiogram analysis

Scientific Reports

V. Thambawita, I. Strümke, S. Hicks, P. Halvorsen, S. Parasa and M. Riegler

Impact of Image Resolution on Deep Learning Performance in Endoscopy Image Classification: An Experimental Study Using a Large Dataset of Endoscopic Images

Diagnostics

D. V. Fryer, I. Strümke and H. Nguyen

Model independent feature attributions: Shapley values that uncover non-linear dependencies

PeerJ Computer Science

D. Fryer, I. Strümke and H. Nguyen

Shapley Values for Feature Selection: The Good, the Bad, and the Axioms

IEEE Access

I. Strümke, M. Slavkovik and V. I. Madai

The social dilemma in artificial intelligence development and why we have to solve it

AI and Ethics

V. Thambawita, I. Strümke, S. Hicks, M. Riegler, P. Halvorsen and S. Parasa

Data Augmentation Using Generative Adversarial Networks For Creating Realistic Artificial Colon Polyp Images

DDW 2021

V. Thambawita, S. Hicks, I. Strümke, M. Riegler, P. Halvorsen and S. Parasa

Impact of Image Resolution on Convolutional Neural Networks Performance in Gastrointestinal Endoscopy

DDW 2021

I. Strümke, S. Hicks, V. Thambawita, D. Jha, S. Parasa, M. Riegler and P. Halvorsen

Artificial Intelligence in Gastroenterology

Artificial Intelligence in Medicine

D. Fryer, I. Strümke and H. Nguyen

Shapley Value Confidence Intervals for Attributing Variance Explained

Frontiers in Applied Mathematics and Statistics

D. Fryer, I. Strümke and H. Nguyen

Shapley Value Confidence Intervals for Attributing Variance Explained

Frontiers in Applied Mathematics and Statistics