Meet ApplyKite Twin, the application assistant that learns with you

Meet your Twin, it learns with you

Your Twin learns with you

Launch week codes:30% off annual Pro20% off monthly Pro

Ends in00h00m00s

Solveig Roth Hoff

Associate Professor

Norwegian University of Science and Technology (NTNU)

Norway

Research Interests

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Explore related searches

Professors in Norway Professors in Medical Imaging Medical Imaging professors in Norway

About

Dr. Solveig Roth Hoff is an Associate Professor at NTNU Institutt for sirkulasjon og bildediagnostikk in Norway. Her research primarily focuses on breast cancer screening methodologies, including artificial intelligence applications in mammography and challenges in comparing imaging techniques. Recent publications highlight her contributions to understanding AI performance in breast cancer detection and the impact of measurement errors in radiological evaluations.

Articles (8)

Year:

2026

Year:

2024

Year:

2024

Year:

2023

Year:

2022

Year:

2022

Year:

2021

Year:

2020

Collaborators (2)

Jan Franz Nygård

Adjunct associate Professor

University of Tromsø

NORWAY

View details

Inger Johanne Bakken

Senior Researcher

Norwegian Institute of Public Health

NORWAY

View details

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Solveig Roth Hoff

Research Interests

Make your next move.

Explore related searches

About

Articles (8)

Effect of Pre-surgical Time and Interfacility Transfer on Mortality in Patients with Ruptured Abdominal Aortic Aneurysm: A Population Based Study

Challenges when comparing tomosynthesis and 2D mammography in breast cancer screening

Performance of an Artificial Intelligence System for Breast Cancer Detection on Screening Mammograms from BreastScreen Norway

AI Risk Score on Screening Mammograms Preceding Breast Cancer Diagnosis

Artificial Intelligence Evaluation of 122 969 Mammography Examinations from a Population-based Screening Program

Editorial for “Breast MRI Background Parenchymal Enhancement Categorization Using Deep Learning: Outperforming the Radiologist”

Radiological review of prior screening mammograms of screen-detected breast cancer

Terminal digit preference: a source of measurement error in breast cancer diameter reporting

Collaborators (2)

Jan Franz Nygård

Inger Johanne Bakken

Make your next move.