Cric searchable image database as a public platform for conventional pap smear cytology data.

Amidst the current health crisis and social distancing, telemedicine has become an important part of mainstream of healthcare, and building and deploying computational tools to support screening more efficiently is an increasing medical priority. The early identification of cervical cancer precursor lesions by Pap smear test can identify candidates for subsequent treatment. However, one of the main challenges is the accuracy of the conventional method, often subject to high rates of false negative.

Saliency-driven system models for cell analysis with deep learning.

Background And Objectives: Saliency refers to the visual perception quality that makes objects in a scene to stand out from others and attract attention. While computational saliency models can simulate the expert's visual attention, there is little evidence about how these models perform when used to predict the cytopathologist's eye fixations. Saliency models may be the key to instrumenting fast object detection on large Pap smear slides under real noisy conditions, artifacts, and cell occlusions.

Quality Indicators of Cervical Cytopathology Tests in the Public Service in Minas Gerais, Brazil.

Objective: The objective of this study is to assess the performance of cytopathology laboratories providing services to the Brazilian Unified Health System (Sistema Único de Saúde - SUS) in the State of Minas Gerais, Brazil.

Methods: This descriptive study uses data obtained from the Cervical Cancer Information System from January to December 2012. Three quality indicators were analyzed to assess the quality of cervical cytopathology tests: positivity index, percentage of atypical squamous cells (ASCs) in abnormal tests, and percentage of tests compatible with high-grade squamous intraepithelial lesions (HSILs).