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Speeding Up Disease Diagnosis

For pathologists, identifying damaged or diseased tissue is a time-consuming process of poring over samples under a microscope. Collaborative research between college veterinarians and electrical engineers at Penn State could significantly speed up the process.

Five pathologists in the Animal Diagnostic Laboratory (ADL) examine more than 10,000 slides annually, according to Art Hattel, veterinary pathologist at ADL. Evaluation can take anywhere from 7 to 25 minutes.

Microscope image.

Using image recognition technology similar to what powers photo-editing software and social media to recognize faces, a team led by Vishal Monga, assistant professor of electrical engineering, with veterinarians at ADL, has developed an automated method of classifying histopathological images.

Monga’s team was provided a series of training slides and worked with ADL staff to learn what pathologists look for in diagnosing
samples.

Custom tools were developed that rely on a sparse encoding of image attributes to help with identification—tools mimicking methods human pathologists use to classify samples.

“We have benchmarked against human judgment, and we are already seeing 80 to 85 percent success in automatically categorizing into three areas: healthy, inflammation, and necrosis,” Monga says.

“We were surprised with how well it picked things up,” Hattel says.

With the initial success using the training images the system could potentially be scaled up to examine other types of abnormalities. To do so the software would require a “training phase” where it is taught what healthy tissue looks like and what diseased tissue looks like. But once the big step of preprocessing is finished, the software could process thousands of images in a second.

In addition to ADL’s large collection of images, the system could incorporate the massive disease databases maintained by the National Institutes of Health, the U.S. government, and other academic and private institutions.

Although the new method isn’t being used by ADL to diagnose any real samples yet, the researchers are applying for more grants to continue the effort in the hopes that one day it might.