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Elife|September 4, 2023
Examining the perceived impact of the COVID-19 pandemic on cervical cancer screening practices among clinicians practicing in Federally Qualified Health Centers: A mixed methods studyLindsay Fuzzell, Paige Lake, Naomi C Brownstein, et al.
Vaccine|May 16, 2020
A prospective study of influenza vaccination and time to pregnancyOlivia R Orta, Elizabeth E Hatch, Annette K Regan, et al.
Journal of Women'S Health (2002)|December 6, 2021
Gaps and Opportunities to Improve Prevention of Human Papillomavirus-Related CancersIrene O Aninye, J Michael Berry-Lawhorn, Paul Blumenthal, et al.
Gynecologic Oncology|August 25, 2022
Comparison of accuracy and reproducibility of colposcopic impression based on a single image versus a two-minute time series of colposcopic imagesRebecca Perkins, Jose Jeronimo, Anne Hammer, et al.
Scientific Reports|December 8, 2023
Reproducible and clinically translatable deep neural networks for cervical screeningSyed Rakin Ahmed, Brian Befano, Andreanne Lemay, et al.
Research Square|March 13, 2023
REPRODUCIBLE AND CLINICALLY TRANSLATABLE DEEP NEURAL NETWORKS FOR CANCER SCREENINGSyed Rakin Ahmed, Brian Befano, Andreanne Lemay, et al.
Scientific Reports|February 21, 2025
Generalizable deep neural networks for image quality classification of cervical imagesSyed Rakin Ahmed, Brian Befano, Didem Egemen, et al.
Journal of the National Cancer Institute|March 19, 2025
Initial evaluation of a new cervical screening strategy combining human papillomavirus genotyping and automated visual evaluation: the Human Papillomavirus-Automated Visual Evaluation ConsortiumBrian Befano, Jayashree Kalpathy-Cramer, Didem Egemen, et al.
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