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Machine Learning and Medicine
Medicine can be as much art as science, a detective story in which doctors rely not only on lab tests and x-rays, but on their own experience and clues from a patient’s history to develop diagnoses or predict future health problems. But all of those lab tests, blood pressure readings, magnetic resonance imaging (MRI) scans, electrocardiograms, and billing codes add up to reams of data, which before too long will be joined by individual gene sequencing. Computer scientists are increasingly applying machine learning techniques to all that data, searching for patterns that can aid diagnosis and improve clinical care.
“Machine learning plays, I think, an essential role in medical image analysis nowadays,” says Kenji Suzuki, assistant professor of radiology and medical physics at the University of Chicago’s Comprehensive Cancer Center. Suzuki has been working on automating the detection of cancerous lesions in images from x-rays or computed tomography scans. Considering that radiologists may miss 12%–30% of lung cancers in such scans, a machine learning tool offers great potential.
Since the mid-1980s, computer scientists have tried to improve on that performance using feature-based machine learning in which the computer would pick out morphological features, texture differences, and more to identify abnormal tissue. But such cataloging of features still misses some cancers that doctors are able to spot with their own eyes. Sometimes the features the computer is seeking can be subtle or overlap with normal anatomical structures such as bone, making them more difficult to spot. So Suzuki asks the machine to instead focus on the values, such as intensity, of individual pixels. “Because the computing power has increased dramatically in recent years, we can process the pixel values directly,” he says. The resulting system is highly sensitive, achieving up to 97% accuracy.
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