CerviCal is the cervical cancer project that uses large databases of Pap smear images for the analysis and pre-screening of massive amounts of cells acquired through an optical microscope. This effort focuses on the research, development, and deployment of tools that have an impact on public health, particularly in under-development countries, such as Brazil. The main goal is to create a suite of computer vision tools to segment and classify cervical cells under occlusion and artifacts common during a Papanicolau procedure. Besides BIDS, this project is also sponsored by CAPES through the Science without Borders award and the Microsoft Azure Machine Learning Research Program.

BIDS Affiliates


Daniela Ushizima

Computational Research Division, Lawrence Berkeley National Lab

Fernando Perez

Co-I for Moore/Sloan Data Science Environments

Saul Perlmutter

Berkeley Institute for Data Science