The goal is to get a 95 percent accuracy rate of discriminating normal mammograms from those that indicate possible cancers. That's as good or better than the best mammographers can do today. The number of mammograms a radiologist interprets daily ranges from several to nearly 200, depending on the size of the clinic and volume of images to process. The expected increase in mammograms could strain the capacity of radiologists to accurately and efficiently diagnose disease. Radiologists would have to work 24 hours a day, seven days a week to meet the demand.
Wavelets are sophisticated algorithms that interpret features of an image or other data.
Artificial neural networks, simply speaking, are based on calculations and patterned after the human brain's system of firing off billions of neurons through a network of synapses. Here's how the Washington University method works. The mammogram images are digitized and turned into pixels that are sized to a format for the processing of the artificial neural network. The wavelets transform the pixels into representations of fine features of the image, separating them into a mathematical hierarchy, much the way a diagram parses a sentence or a family tree traces lineage. Eventually, the wavelets separate the features of the images into two piles, one the standard, or control, pile, the other the anomalous pile, where disease may lurk.