Per Magne
Florvaag
Research Engineer
Publications
2021
Poster
B-PO02-022 Combining simulation and machine learning to accurately predict arrhythmic risk in post-infarction patients
In Heart Rhythm. Vol. 18. Boston, MA: Elsevier, 2021.Status: Published
B-PO02-022 Combining simulation and machine learning to accurately predict arrhythmic risk in post-infarction patients
In Heart Rhythm. Vol. 18. Boston, MA: Elsevier, 2021.
Status: Published
| Afilliation | Scientific Computing |
| Project(s) | Department of Computational Physiology |
| Publication Type | Poster |
| Year of Publication | 2021 |
| Secondary Title | Heart Rhythm |
| Publisher | Elsevier |
| Place Published | Boston, MA |
Journal Article
Combined In-silico and Machine Learning Approaches Toward Predicting Arrhythmic Risk in Post-infarction Patients
Frontiers in physiology 12 (2021): 745349.Status: Published
Combined In-silico and Machine Learning Approaches Toward Predicting Arrhythmic Risk in Post-infarction Patients
Frontiers in physiology 12 (2021): 745349.
Status: Published
| Afilliation | Scientific Computing |
| Project(s) | Department of Computational Physiology |
| Publication Type | Journal Article |
| Year of Publication | 2021 |
| Journal | Frontiers in physiology |
| Volume | 12 |
| Pagination | 745349 |
| Publisher | Frontiers |
Nationwide rollout reveals efficacy of epidemic control through digital contact tracing
Nature Communications 12 (2021).Status: Published
Nationwide rollout reveals efficacy of epidemic control through digital contact tracing
Nature Communications 12 (2021).
Status: Published
| Afilliation | Communication Systems, Scientific Computing, Machine Learning |
| Project(s) | The Center for Resilient Networks and Applications, Department of Data Science and Knowledge Discovery , Department of Computational Physiology |
| Publication Type | Journal Article |
| Year of Publication | 2021 |
| Journal | Nature Communications |
| Volume | 12 |
| Number | 5918 |
| Publisher | Springer Nature |
| DOI | 10.1038/s41467-021-26144-8 |
2019
Talks, contributed
A Combined In-Silico and Machine Learning Approach towards Predicting Arrhythmic Risk in Post-Infarction Patients
In Computing in Cardiology, Singapore, 2019.Status: Published
A Combined In-Silico and Machine Learning Approach towards Predicting Arrhythmic Risk in Post-Infarction Patients
In Computing in Cardiology, Singapore, 2019.
Status: Published
| Afilliation | Scientific Computing |
| Project(s) | MI-RISK: Risk factors for sudden cardiac death during acute myocardial infarction , Department of Computational Physiology |
| Publication Type | Talks, contributed |
| Year of Publication | 2019 |
| Location of Talk | Computing in Cardiology, Singapore |
Proceedings, refereed
Automated and objective segmentation of medical image using machine learning techniques: all models are wrong, but some are useful
In Computational and Mathematical Biomedical Engineering. Sendai, Japan: CMBE, 2019.Status: Published
Automated and objective segmentation of medical image using machine learning techniques: all models are wrong, but some are useful
In Computational and Mathematical Biomedical Engineering. Sendai, Japan: CMBE, 2019.
Status: Published
Abstract
Medical images are the basis of ”patient-specific” simulations but come with severe limitations, most notably through operator dependencies like image segmentation. The aim was to develop an open- source pipeline for automated and objective segmentation. Combining latest advances from machine learning and signal processing, we demonstrate that the pipeline preserve all major characteristic features of a test image and identify minor branches, which can be further modified by the user. In conclusion, the default pipeline will in the majority of cases offer labor free automated and objective segmentation, or at worst provide an optimal starting point for manual segmentation.
| Afilliation | Scientific Computing, Machine Learning |
| Project(s) | Simula Metropolitan Center for Digital Engineering, Department of Computational Physiology |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2019 |
| Conference Name | Computational and Mathematical Biomedical Engineering |
| Publisher | CMBE |
| Place Published | Sendai, Japan |
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