Globally Deployed Cervical Cancer AI Syed Rakin Ahmed
Papers
Generalizable deep neural networks for image quality classification of cervical images.
Assessing generalizability of an AI-based visual test for cervical cancer screening.
Reproducible and clinically translatable deep neural networks for cervical screening.
Use of risk-based cervical screening programs in resource-limited settings.
AI-based image analysis in clinical testing: lessons from cervical cancer screening.
Design of the HPV-Automated Visual Evaluation (PAVE) Study: Validating a Novel Cervical Screening Strategy.
Conformal prediction and Monte Carlo inference for addressing uncertainty in cervical cancer screening
Applying self-supervision and conformal prediction for cervical cancer screening.
Design and multi-rater evaluation of a cervical visual treatability classifier
Presentations and Symposia
Implications for squamocolumnar junction visibility on the performance of Automated Visual Evaluation algorithm for cervical cancer screening
Assessing cervical image quality for an AI-based Automated Visual Evaluation (AVE) algorithm in the PAVE strategy.
Deep learning-based cervical cancer visual evaluation test: designing the test result as 2-class or 3-class.