Researchers at Lehigh University say they have developed a cervical cancer screening technique that, based on an analysis of a very large dataset, could perform as well as, or better than, human interpretation or other traditional screening methods at a much reduced cost.
The system, developed over 10 years by Lehigh professor Xiaolei Huang's team, is built on image-based classifier algorithms constructed from a large number of Cervigrams.
The researchers hypothesized that algorithms could help enhance the precision in grading lesions by using visual information.
Huang says she creates "techniques that enable computers to understand images the way humans do," and one of her primary interests is training computers to understand biomedical images.
"Cervigrams have great potential as a screening tool in resource-poor regions where clinical tests such as Pap and [human papilloma virus] are too expensive to be made widely available," Huang notes.
From Lehigh University
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