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F. Fineide, A. Storås, M. Magnø, H. L. Hammer, X. Chen, A. Khan, P. Halvorsen, T. Utheim and M. Riegler

Predicting the degree of meibomian gland dropout with artificial intelligence

In ARVO Annual Meeting, 2023.
Proceedings, refereed
2023
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A. Storås, F. Fineide, H. L. Hammer, A. Khan, M. Magnø, C. Xiangjung, P. Halvorsen, T. Utheim and M. Riegler

Using explainable artificial intelligence (XAI) to explore factors affecting meibomian gland (MG) dropout

In ARVO Annual Meeting, 2023.
Proceedings, refereed
2023
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H. L. Hammer, M. Riegler and H. Tjelmeland

Approximate Bayesian Inference Based on Expected Evaluation

Bayesian Analysis 1, no. 1 (2023).
Journal Article
2023
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A. Sharma, M. Stensen, E. Delbarre, M. Siddiqui, T. Haugen, M. Riegler and H. L. Hammer

Detecting human embryo cleavage stages using YOLO v5 object detection algorithm

In Nordic Artificial Intelligence Research and Development. Springer, 2023.
Proceedings, refereed
2023
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2021

Journal articles

A. A. Mofrad, A. Yazidi, S. A. Mofrad, H. L. Hammer and E. Arntzen

Enhanced Equivalence Projective Simulation: A Framework for Modeling Formation of Stimulus Equivalence Classes

Neural Computation 33, no. 2 (2021): 483-527.
Status: Published
M. Riegler, M. H. Stensen, O. Witczak, J. M. Andersen, S. Hicks, H. L. Hammer, E. Delbarre, P. Halvorsen, A. Yazidi, N. Holst et al.

Artificial intelligence in the fertility clinic: status, pitfalls and possibilities

Human Reproduction 36, no. 9 (2021): 2429-2442.
Status: Published
A. Sharma, T. Haugen, H. L. Hammer, M. Riegler and M. Stensen

Towards better explainable deep learning models for embryo selection in ART

Human Reproduction 36, no. Supplement_1 (2021).
Status: Published
P. H. Smedsrud, V. Thambawita, S. Hicks, H. Gjestang, O. O. Nedrejord, E. Næss, H. Borgli, D. Jha, T. J. D. Berstad, S. L. Eskeland et al.

Kvasir-Capsule, a video capsule endoscopy dataset

Scientific Data 8, no. 1 (2021): 142.
Status: Published
H. Svennevik, M. Riegler, S. Hicks, T. Storelvmo and H. L. Hammer

Prediction of Cloud Fractional Cover Using Machine Learning

Big Data and Cognitive Computing 5, no. 4 (2021): 62.
Status: Published
V. Thambawita, J. L. Isaksen, S. Hicks, J. Ghouse, G. Ahlberg, A. Linneberg, N. Grarup, C. Ellervik, M. S. Olesen, T. Hansen et al.

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

Scientific Reports (2021).
Status: Accepted
V. Thambawita, J. L. Isaksen, S. Hicks, J. Ghouse, G. Ahlberg, A. Linneberg, N. Grarup, C. Ellervik, M. S. Olesen, T. Hansen et al.

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

Nature Scientific Reports 11 (2021): 21896.
Status: Published

Proceedings, refereed

S. S. Eide, M. Riegler, H. L. Hammer and J. B. Bremnes

Temperature Forecasting using Tower Networks

In Proceedings of the 2021 Workshop on Intelligent Cross-Data Analysis and Retrieval. New York, NY, USA: Association for Computing Machinery, 2021.
Status: Published
E. Garcia-Ceja, V. Thambawita, S. Hicks, D. Jha, P. Jakobsen, H. L. Hammer, P. Halvorsen and M. Riegler

HTAD: A Home-Tasks Activities Dataset with Wrist-accelerometer and Audio Features

In 27th International Conference on Multimedia Modeling. Springer, 2021.
Status: Published
S. Hicks, A. Stautland, O. B. Fasmer, W. Frøland, H. L. Hammer, P. Halvorsen, K. Mjeldheim, K. J. Oedegaard, B. Osnes, V. E. G. Syrstad et al.

HYPERAKTIV: An Activity Dataset from Patients with Attention-Deficit/Hyperactivity Disorder (ADHD)

In Proceedings of the 12th ACM Multimedia Systems Conference (MMSys '21). ACM, 2021.
Status: Published