Multicontrast Pocket Colposcopy Cervical Cancer Diagnostic Algorithm for Referral Populations.

ABSTRACT: Objective and Impact Statement. We use deep learning models to classify cervix images-collected with a low-cost, portable Pocket colposcope-with biopsy-confirmed high-grade precancer and cancer. We boost classification performance on a screened-positive population by using a class-balanced loss and incorporating green-light colposcopy image pairs, which come at no additional cost to the provider. Introduction. Because the majority of the 300,000 annual deaths due to cervical cancer occur in countries with low- or middle-Human Development Indices, an automated classification algorithm could overcome limitations caused by the low prevalence of trained professionals and diagnostic variability in provider visual interpretations. Methods. Our dataset consists of cervical im

SUBMITTER: Skerrett E 

PROVIDER: S-EPMC10521679 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature