Výsledky vyhledávání - "Aasa, Feragen"

Akademický článek

AI supported fetal echocardiography with quality assessment

Autor: Caroline A. Taksoee-Vester, Kamil Mikolaj, Zahra Bashir, Anders N. Christensen, Olav B. Petersen, Karin Sundberg, Aasa Feragen, Morten B. S. Svendsen, Mads Nielsen, Martin G. Tolsgaard

Publikováno v: Scientific Reports, Vol 14, Iss 1, Pp 1-9 (2024)

Abstract This study aimed to develop a deep learning model to assess the quality of fetal echocardiography and to perform prospective clinical validation. The model was trained on data from the 18–22-week anomaly scan conducted in seven hospitals f

Externí odkaz: https://doaj.org/article/893b8353e9154aa598fcf9ba1b3580c9

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Akademický článek

Leveraging Education Science for AI-Clinician Collaboration in the Patient Care Ecosystem

Autor: Martin Grønnebæk Tolsgaard, DMSc, Aasa Feragen, PhD, Lawrence Grierson, PhD

Publikováno v: Annals of Surgery Open, Vol 5, Iss 2, p e442 (2024)

Externí odkaz: https://doaj.org/article/e1f4b17597ed40caaffda4988c486d3b

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Akademický článek

Multi-centre deep learning for placenta segmentation in obstetric ultrasound with multi-observer and cross-country generalization

Autor: Lisbeth Anita Andreasen, Aasa Feragen, Anders Nymark Christensen, Jonathan Kistrup Thybo, Morten Bo S. Svendsen, Kilian Zepf, Karim Lekadir, Martin Grønnebæk Tolsgaard

Publikováno v: Scientific Reports, Vol 13, Iss 1, Pp 1-8 (2023)

Abstract The placenta is crucial to fetal well-being and it plays a significant role in the pathogenesis of hypertensive pregnancy disorders. Moreover, a timely diagnosis of placenta previa may save lives. Ultrasound is the primary imaging modality i

Externí odkaz: https://doaj.org/article/04b9fd21165b42d7a44bdd946462a050

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Akademický článek

Q-space trajectory imaging with positivity constraints (QTI+)

Autor: Magnus Herberthson, Deneb Boito, Tom Dela Haije, Aasa Feragen, Carl-Fredrik Westin, Evren Özarslan

Publikováno v: NeuroImage, Vol 238, Iss , Pp 118198- (2021)

Q-space trajectory imaging (QTI) enables the estimation of useful scalar measures indicative of the local tissue structure. This is accomplished by employing generalized gradient waveforms for diffusion sensitization alongside a diffusion tensor dist

Externí odkaz: https://doaj.org/article/b5e6dff6b3d0483393738d0e6a0a3da3

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Akademický článek

Enforcing necessary non-negativity constraints for common diffusion MRI models using sum of squares programming

Autor: Tom Dela Haije, Evren Özarslan, Aasa Feragen

Publikováno v: NeuroImage, Vol 209, Iss , Pp 116405- (2020)

In this work we investigate the use of sum of squares constraints for various diffusion-weighted MRI models, with a goal of enforcing strict, global non-negativity of the diffusion propagator. We formulate such constraints for the mean apparent propa

Externí odkaz: https://doaj.org/article/6ee8165104c3482194a5fb61fafae1fb

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Populations of Unlabelled Networks: Graph Space Geometry and Generalized Geodesic Principal Components

Autor: Anna Calissano, Aasa Feragen, Simone Vantini

Publikováno v: Biometrika.

Summary Statistical analysis for populations of networks is widely applicable but challenging as networks have strongly non-Euclidean behaviour. Graph space is an exhaustive framework for studying populations of unlabelled networks which are weighted

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::734587947cfbf15e673e3ed86be3240d
https://doi.org/10.1093/biomet/asad024

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On (assessing) the fairness of risk score models

Autor: Eike Petersen, Melanie Ganz, Sune Hannibal Holm, Aasa Feragen

Publikováno v: University of Copenhagen

Recent work on algorithmic fairness has largely focused on the fairness of discrete decisions, or classifications. While such decisions are often based on risk score models, the fairness of the risk models themselves has received considerably less at

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::037372d9b1646070343ba01206d3e88c
http://arxiv.org/abs/2302.08851

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Autor: Steffen Czolbe, Paraskevas Pegios, Oswin Krause, Aasa Feragen

Publikováno v: Czolbe, S, Pegios, P, Krause, O & Feragen, A 2023, ' Semantic similarity metrics for image registration ', Medical Image Analysis, vol. 87, 102830 . https://doi.org/10.1016/j.media.2023.102830

Image registration aims to find geometric transformations that align images. Most algorithmic and deep learning-based methods solve the registration problem by minimizing a loss function, consisting of a similarity metric comparing the aligned images

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a6d5f375a8c1c0dc5c913e152d10a09b
https://orbit.dtu.dk/en/publications/7a793a13-9569-4174-86f9-7897adfc126d

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