| 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 | 249 | 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 | 141 | 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 | 90 | 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 | 75 | 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 | 59 | 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 | 54 | 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 | 28 | 2021 |
| 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 | 22 | 2016 |
| 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 | 21 | 2020 |
| 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, ... Radiology 297 (2), 327-333, 2020 | 16 | 2020 |
| 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 | 15 | 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, JCO. 21.01337, 2021 | 12 | 2021 |
| 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 | 12 | 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 | 11 | 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 | 10 | 2018 |
| 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 | 10 | 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 | 9 | 2019 |
| Adding seemingly uninformative labels helps in low data regimes C Matsoukas, AB Hernandez, Y Liu, K Dembrower, G Miranda, E Konuk, ... International Conference on Machine Learning, 6775-6784, 2020 | 5 | 2020 |
| Predictive value of breast MRI background parenchymal enhancement for neoadjuvant treatment response among HER2− patients VA Arasu, P Kim, W Li, F Strand, C McHargue, R Harnish, DC Newitt, ... Journal of Breast Imaging 2 (4), 352-360, 2020 | 5 | 2020 |
| 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 | 5 | 2020 |
