CSE assistant professor Xiaolei Huang aims to harness AI to improve medical imaging.
Artificial intelligence—commonly known as AI—is already exceeding human abilities. Self-driving cars use AI to perform some tasks more safely than people. E-commerce companies use AI to tailor product ads to customers’ tastes more quickly and precisely than any breathing marketing analyst can.
And soon AI will be used to “read” biomedical images more accurately than medical personnel alone—providing better early cervical cancer detection at lower cost than current methods.
However, this does not necessarily mean radiologists will soon be out of business.
“Humans and computers are very complementary,” says Xiaolei Huang, associate professor ofcomputer science and engineering. “That’s what AI is all about.”
Huang directs the Image Data Emulation and Analysis Laboratory, where she works on artificial intelligence related to vision and graphics, or, as she says, “creating techniques that enable computers to understand images the way humans do.” Among Huang’s primary interests is training computers to understand biomedical images.
Now, as a result of 10 years work, Huang and her team have created a cervical cancer screening technique that, based on an analysis of a very large dataset, has the potential to perform as well as, or better than, human interpretation or other traditional screening results, such as Pap tests and tests for human papilloma virus (HPV)—at a much lower cost. The technique could be used in less developed countries, where 80 percent of deaths from cervical cancer occur.
However, this does not necessarily mean radiologists will soon be out of business.
“Humans and computers are very complementary,” says Xiaolei Huang, associate professor ofcomputer science and engineering. “That’s what AI is all about.”
Huang directs the Image Data Emulation and Analysis Laboratory, where she works on artificial intelligence related to vision and graphics, or, as she says, “creating techniques that enable computers to understand images the way humans do.” Among Huang’s primary interests is training computers to understand biomedical images.
Now, as a result of 10 years work, Huang and her team have created a cervical cancer screening technique that, based on an analysis of a very large dataset, has the potential to perform as well as, or better than, human interpretation or other traditional screening results, such as Pap tests and tests for human papilloma virus (HPV)—at a much lower cost. The technique could be used in less developed countries, where 80 percent of deaths from cervical cancer occur.
Huang’s screening system is built on image-based classifiers (an algorithm that classifies data) constructed from a large number of Cervigrams. Cervigrams are images taken by digital cervicography, a noninvasive visual examination method that takes a photograph of the cervix. The images, when read, are designed to detect cervical intraepithelial neoplasia (CIN), which is the potentially precancerous change and abnormal growth of squamous cells on the surface of the cervix.
“Cervigrams have great potential as a screening tool in resource-poor regions where clinical tests such as Pap and HPV are too expensive to be made widely available,” says Huang. “However, there is concern about Cervigrams’ overall effectiveness due to reports of poor correlation between visual lesion recognition and high-grade disease, as well as disagreement among experts when grading visual findings.”
“Cervigrams have great potential as a screening tool in resource-poor regions where clinical tests such as Pap and HPV are too expensive to be made widely available,” says Huang. “However, there is concern about Cervigrams’ overall effectiveness due to reports of poor correlation between visual lesion recognition and high-grade disease, as well as disagreement among experts when grading visual findings.”
Huang thought that computer algorithms could help improve accuracy in grading lesions by using visual information—a hunch that, so far, is proving correct.
They describe their results in an article in the March issue of Pattern Recognition titled “Multi-feature base benchmark for cervical dysplasia classification.”
They describe their results in an article in the March issue of Pattern Recognition titled “Multi-feature base benchmark for cervical dysplasia classification.”
Read the full story at the Lehigh University News Center.
-Lori Friedman is Director of Media Relations in the Office of Communications and Public Affairs at Lehigh University.
May 9, 2017
