Andrea Storås

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

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

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 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

T. Nguyen, A. Storås, V. Thambawita, S. Hicks, P. Halvorsen and M. Riegler

Multimedia datasets: challenges and future possibilities

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, 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 and J. Sundgaard

Concept Explanations for Deep Learning-Based Diabetic Retinopathy Diagnosis

Concept Explanations for Deep Learning-Based Diabetic Retinopathy Diagnosis

A. Storås

Explainable Artificial Intelligence and Dry Eyes

Seminar at department of medical biochemistry, Oslo university hospital

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

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

Bias og kvantitativ analyse innen velferd

Tidsskrift for velferdsforskning

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

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

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

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)

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

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)

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

Unsupervised Image Segmentation via Self-Supervised Learning Image Classification

MediaEval 2021

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

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