November 5, 2013
Machines Learn To Detect Breast Cancer
Software that can recognize patterns in data is commonly used by scientists and economics. Now, researchers in the US have applied similar algorithms to help them more accurately diagnose breast cancer. The researchers outline details in the International Journal of Medical Engineering and Informatics.
Duo Zhou a biostatistician at pharmaceutical company Pfizer in New York and colleagues Dinesh Mital and Shankar Srinivasan of the University of Medicine and Dentistry of New Jersey, point out that data pattern recognition is widely used in machine-learning applications in science. Computer algorithms trained on historical data can be used to analyze current information and detect patterns and then predict possible future patterns. However, this powerful knowledge discovery technology is little used in medicine.
The machine learning approach takes into account nine characteristics of a minimally invasive fine needle biopsy, including clump thickness, uniformity of cell size, adhesions, epithelial cell size, bare cell nuclei and other factors. Trained on definitive data annotated as malignant or benign, the system was able to correlate the many disparate visual factors present in the data with the outcome. The statistical model thus developed could then be used to test new tissue samples for malignancy.
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