Andrea Storås
PhD student
- Department
- Holistic Systems
- Organisation
- Simula Metropolitan
- Research Interests
- Medicine, pharmacy, machine learning and explainable artificial intelligence

- andrea@simula.no
Publications
2023
Journal Articles
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
V. Thambawita, S. Hicks, A. Storås, T. Nguyen, J. M. Andersen, O. Witczak, T. B. Haugen, H. L. Hammer, P. Halvorsen and M. Riegler
VISEM-Tracking, a human spermatozoa tracking dataset
Scientific Data
Posters
A. Storås and J. Sundgaard
Concept Explanations for Deep Learning-Based Diabetic Retinopathy Diagnosis
Concept Explanations for Deep Learning-Based Diabetic Retinopathy Diagnosis
Proceedings, refereed
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
ARVO Annual Meeting
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)
T. Nguyen, A. Storås, V. Thambawita, S. Hicks, P. Halvorsen and M. Riegler
Multimedia datasets: challenges and future possibilities
International conference on multimedia modeling
A. H. Z. Nik, M. Riegler, P. Halvorsen and A. Storås
Generation of Synthetic Tabular Healthcare Data Using Generative Adversarial Networks
International conference on multimedia modeling
F. Fineide, A. Storås, M. Riegler and T. Utheim
Predicting Meibomian Gland Dropout and Feature Importance Analysis with Explainable Artificial Intelligence
IEEE International Symposium on Computer-Based Medical Systems (IEEE CBMS2023)
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
ARVO Annual Meeting
A. Storås and J. Sundgaard
Looking into Concept Explanation Methods for Diabetic Retinopathy Classification
Workshop on Interpretability of Machine Intelligence in Medical Image Computing at MICCAI 2023
V. Thambawita, A. Storås, S. Hicks, P. Halvorsen and M. Riegler
MLC at HECKTOR 2022: The Effect and Importance of Training Data When Analyzing Cases of Head and Neck Tumors Using Machine Learning
Head and Neck Tumor Segmentation and Outcome Prediction: Third Challenge, HECKTOR 2022, Held in Conjunction with MICCAI 2022
Talks, invited
A. Storås
Explainable Artificial Intelligence and Dry Eyes
Seminar at department of medical biochemistry, Oslo university hospital
2022
Journal Articles
V. Thambawita, S. Hicks, A. Storås, O. Witczak, J. M. Andersen, H. L. Hammer, P. Halvorsen, M. Riegler and H. B. Trine
Real-time deep learning based multi object tracking of spermatozoa in fresh samples
Human Reproduction
S. Hicks, V. Thambawita, A. Storås, T. B. Haugen, H. L. Hammer, P. Halvorsen, M. Riegler and M. H. Stensen
Automatic Tracking of the ICSI procedure using Deep Learning
Human Reproduction
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
F. Fineide, A. Storås, X. Chen, M. S. Magnø, A. Yazidi, M. Riegler and T. P. Utheim
Predicting an unstable tear film through artificial intelligence
Scientific Reports
A. Storås, R. Prabhu, H. L. Hammer and I. Strümke
Bias og kvantitativ analyse innen velferd
Tidsskrift for velferdsforskning
Miscellaneous
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
Posters
A. Storås, M. Riegler, P. Halvorsen, A. Åsberg and I. Strümke
Predicting drug exposure in kidney transplanted patients using machine learning
Proceedings, refereed
A. Storås
Unsupervised Image Segmentation via Self-Supervised Learning Image Classification
MediaEval 2021
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)
J. A. Fageren, V. Thambawita, A. Storås, S. Parasa, T. de Lange, P. Halvorsen and M. Riegler
PolypConnect: Image inpainting for generating realistic gastrointestinal tract images with polyps
IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)
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, I. Strümke, M. Riegler and P. Halvorsen
Explainability methods for machine learning systems for multimodal medical datasets: research proposal
ACM Multimedia Systems (MMSys) Conference
A. Storås, M. Riegler, T. B. Haugen, V. Thambawita, S. Hicks, H. L. Hammer, R. Kakulavarapu, P. Halvorsen and M. Stensen
Automatic Unsupervised Clustering of Videos of the Intracytoplasmic Sperm Injection (ICSI) Procedure
NAIS: Symposium of the Norwegian AI Society
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
V. Thambawita, S. Hicks, A. Storås, J. M. Andersen, O. Witczak, T. B. Haugen, H. L. Hammer, T. Nguyen, P. Halvorsen and M. Riegler
Medico Multimedia Task at MediaEval 2022: Transparent Tracking of Spermatozoa
MediaEval 2022
Talks, invited
A. Storås, I. Strümke, M. Riegler and P. Halvorsen
Explainable Artificial Intelligence in Medicine
Nordic AI Meet 2022