Work

Combining multiple contrasts for improving machine learning-based classification of cervical cancers with a low-cost point-of-care Pocket colposcope

Year: 2020

Type: conference-paper

Abstract: We apply feature-extraction and machine learning methods to multiple sources of contrast (acetic acid, Lugol's iodine and green light) from the white Pocket Colposcope, a low-cost point of care colposcope for cervical cancer screening. We combine features from the sources of contrast and analyze diagnostic improvements with addition of each contrast. We find that overall AUC increases with additional contrast agents compared to using only one source.

Source: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society

DOI: 10.1109/embc44109.2020.9175858

Authors: Mercy N. Asiedu, Erica Skerrett, Guillermo Sapiro, Nirmala Ramanujam

Institutions: Duke University, MIT Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology

Language: English

Topic: AI in cancer detection

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: bronze

Funders: National Institutes of Health, National Cancer Institute