What Peng and the team did was to look at digital mammograms of breasts with tiny calcifications measuring some 0.3 millimeters in size. They then ran a series of publicly available algorithms known to detect and mark breast cancer on digital mammograms. When they matched these markings against ones made by their "noise" adding algorithms, they found the stochastic resonance-based program was much less likely to mistake benign growths for deadly tumors.

With their detectors, there was a 36 percent reduction in false positives, even though the sensitivity -- the ability to detect real cancers -- was the same or better than for other systems.

"It's just a prototype," says Chen, "but we demonstrated some potential. We believe it can be applied to a broad spectrum of applications, such as cochlear implants or systems to improve touch."

As for offers from OEMs, Chen says he's open, and some inquiries have already been made through an office at the university.

"We were hoping that someday we can collaborate with some companies to actually make a product that will be beneficial for society," he adds.

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