Assessing generalizability of an AI-based visual test for cervical cancer screening
Assessing generalizability of an AI-based visual test for cervical cancer screening
About this item
Full title
Author / Creator
Ahmed, Syed Rakin , Egemen, Didem , Befano, Brian , Rodriguez, Ana Cecilia , Jeronimo, Jose , Desai, Kanan , Teran, Carolina , Alfaro, Karla , Fokom-Domgue, Joel , Charoenkwan, Kittipat , Mungo, Chemtai , Luckett, Rebecca , Saidu, Rakiya , Raiol, Taina , Ribeiro, Ana , Gage, Julia C. , de Sanjose, Silvia , Kalpathy-Cramer, Jayashree and Schiffman, Mark
Publisher
United States: Public Library of Science
Journal title
Language
English
Formats
Publication information
Publisher
United States: Public Library of Science
Subjects
More information
Scope and Contents
Contents
A number of challenges hinder artificial intelligence (AI) models from effective clinical translation. Foremost among these challenges is the lack of generalizability, which is defined as the ability of a model to perform well on datasets that have different characteristics from the training data. We recently investigated the development of an AI p...
Alternative Titles
Full title
Assessing generalizability of an AI-based visual test for cervical cancer screening
Authors, Artists and Contributors
Author / Creator
Egemen, Didem
Befano, Brian
Rodriguez, Ana Cecilia
Jeronimo, Jose
Desai, Kanan
Teran, Carolina
Alfaro, Karla
Fokom-Domgue, Joel
Charoenkwan, Kittipat
Mungo, Chemtai
Luckett, Rebecca
Saidu, Rakiya
Raiol, Taina
Ribeiro, Ana
Gage, Julia C.
de Sanjose, Silvia
Kalpathy-Cramer, Jayashree
Schiffman, Mark
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_a4826030e45540a2b3e411587d53f3fe
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_a4826030e45540a2b3e411587d53f3fe
Other Identifiers
ISSN
2767-3170
E-ISSN
2767-3170
DOI
10.1371/journal.pdig.0000364
