Fredrik Strand
Fredrik Strand
Karolinska University Hospital
Verified email at - Homepage
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Common and unique components of inhibition and working memory: an fMRI, within-subjects investigation
F McNab, G Leroux, F Strand, L Thorell, S Bergman, T Klingberg
Neuropsychologia 46 (11), 2668-2682, 2008
Evaluation of combined artificial intelligence and radiologist assessment to interpret screening mammograms
T Schaffter, DSM Buist, CI Lee, Y Nikulin, D Ribli, Y Guan, W Lotter, Z Jie, ...
JAMA network open 3 (3), e200265-e200265, 2020
Phonological working memory with auditory presentation of pseudo-words—an event related fMRI Study
F Strand, H Forssberg, T Klingberg, F Norrelgen
Brain research 1212, 48-54, 2008
External evaluation of 3 commercial artificial intelligence algorithms for independent assessment of screening mammograms
M Salim, E Wċhlin, K Dembrower, E Azavedo, T Foukakis, Y Liu, K Smith, ...
JAMA oncology 6 (10), 1581-1588, 2020
Effect of artificial intelligence-based triaging of breast cancer screening mammograms on cancer detection and radiologist workload: a retrospective simulation study
K Dembrower, E Wċhlin, Y Liu, M Salim, K Smith, P Lindholm, M Eklund, ...
The Lancet Digital Health 2 (9), e468-e474, 2020
Comparison of a deep learning risk score and standard mammographic density score for breast cancer risk prediction
K Dembrower, Y Liu, H Azizpour, M Eklund, K Smith, P Lindholm, F Strand
Radiology 294 (2), 265-272, 2020
Toward robust mammography-based models for breast cancer risk
A Yala, PG Mikhael, F Strand, G Lin, K Smith, YL Wan, L Lamb, K Hughes, ...
Science Translational Medicine 13 (578), eaba4373, 2021
A multi-million mammography image dataset and population-based screening cohort for the training and evaluation of deep neural networks—the cohort of screen-aged women (CSAW)
K Dembrower, P Lindholm, F Strand
Journal of digital imaging 33 (2), 408-413, 2020
Novel mammographic image features differentiate between interval and screen-detected breast cancer: a case-case study
F Strand, K Humphreys, A Cheddad, S Törnberg, E Azavedo, J Shepherd, ...
Breast Cancer Research 18 (1), 1-10, 2016
Identification of Women at High Risk of Breast Cancer Who Need Supplemental Screening.
M Eriksson, K Czene, F Strand, S Zackrisson, P Lindholm, K Lċng, ...
Predicting breast cancer response to neoadjuvant treatment using multi-feature MRI: results from the I-SPY 2 TRIAL
W Li, DC Newitt, J Gibbs, LJ Wilmes, EF Jones, VA Arasu, F Strand, ...
NPJ breast cancer 6 (1), 1-6, 2020
Multi-institutional validation of a mammography-based breast cancer risk model
A Yala, PG Mikhael, F Strand, G Lin, S Satuluru, T Kim, I Banerjee, ...
Journal of Clinical Oncology 40 (16), 1732-1740, 2022
Range of radiologist performance in a population-based screening cohort of 1 million digital mammography examinations
M Salim, K Dembrower, M Eklund, P Lindholm, F Strand
Radiology 297 (1), 33-39, 2020
The future of breast cancer screening: what do participants in a breast cancer screening program think about automation using artificial intelligence?
O Jonmarker, F Strand, Y Brandberg, P Lindholm
Acta radiologica open 8 (12), 2058460119880315, 2019
Long-term prognostic implications of risk factors associated with tumor size: a case study of women regularly attending screening
F Strand, K Humphreys, J Holm, M Eriksson, S Törnberg, P Hall, ...
Breast Cancer Research 20 (1), 1-10, 2018
Evaluation of combined artificial intelligence and radiologist assessment to interpret screening mammograms. JAMA Netw Open. 2020; 3 (3): 200265
T Schaffter, DSM Buist, CI Lee, Y Nikulin, D Ribli, Y Guan, W Lotter, Z Jie, ...
Longitudinal fluctuation in mammographic percent density differentiates between interval and screen‐detected breast cancer
F Strand, K Humphreys, M Eriksson, J Li, TML Andersson, S Törnberg, ...
International Journal of Cancer 140 (1), 34-40, 2017
Localized mammographic density is associated with interval cancer and large breast cancer: a nested case-control study
F Strand, E Azavedo, R Hellgren, K Humphreys, M Eriksson, J Shepherd, ...
Breast Cancer Research 21 (1), 1-9, 2019
Comparison of segmentation methods in assessing background parenchymal enhancement as a biomarker for response to neoadjuvant therapy
AAT Nguyen, VA Arasu, F Strand, W Li, N Onishi, J Gibbs, EF Jones, ...
Tomography 6 (2), 101-110, 2020
Optimizing risk-based breast cancer screening policies with reinforcement learning
A Yala, PG Mikhael, C Lehman, G Lin, F Strand, YL Wan, K Hughes, ...
Nature Medicine 28 (1), 136-143, 2022
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