New deep-learning approach gets to the bottom of colonoscopy
Researchers have developed a pair of modules that gives a boost to the use of artificial neural networks to identify potentially cancerous growths in colonoscopy imagery, traditionally plagued by image noise resulting from the colonoscopy insertion and rotation process itself.
A paper describing the approach was published in the journal CAAI Artificial Intelligence Research on June 30.
Colonoscopy is the gold standard for detecting colorectal growths or ‘polyps’ in the inner lining of your colon, also known as the large intestine. ...











