Mine-hunting Software Can Revolutionize Identifying Human Cancer Cells
A new software developed for finding and recognizing undersea mines can help doctors identify and classify cancer-related cells.
“The results are spectacular,” said Dr. Larry Carin, professor at Duke University and developer of the technology. “This could be a game-changer for medical research.”
The problem that physicians encounter in analyzing images of human cells is surprisingly similar to the Navy’s challenge of finding undersea mines.
When examining tissue samples, doctors must sift through hundreds of microscopic images containing millions of cells.
To pinpoint specific cells of interest, they use an automated image analysis software toolkit called FARSIGHT, or Fluorescence Association Rules for Quantitative Insight.
Funded by the National Institutes of Health (NIH) and the Defense Advanced Research Projects Agency (DARPA), FARSIGHT identifies cells based upon a subset of examples initially labeled by a physician. But the resulting classifications can be erroneous because the computer applies tags based on the small sampling.
By adding ONR’s active learning software algorithms, the identification of cells is more accurate and FARSIGHT’s performance more consistent, researchers said.
A medical team at the University of Pennsylvania is applying the ONR algorithms, embedded into FARSIGHT, to examine tumors from kidney cancer patients.
Focusing on endothelial cells that form the blood vessels that supply the tumors with oxygen and nutrients, the research could one day improve drug treatments for different types of kidney cancer, also known as renal cell carcinoma.
