Massive-training artificial neural networks for CAD for detection of polyps in CT colonography: False-negative cases in a large multicenter clinical trial

Abstract

A major challenge in computer-aided detection (CAD) of polyps in CT colonography (CTC) is the detection of "difficult" polyps which radiologists are likely to miss. Our purpose was to develop massive-training artificial neural networks (MTANNs) for improving the performance of a CAD scheme on false-negative cases in a large multicenter clinical trial. We developed 3D MTANNs designed to differentiate between polyps and several types of non- polyps and tested on 14 polyps/masses that were actually "missed" by radiologists in the trial. Our initial CAD scheme detected 71.4% of "missed" polyps with 18.9 false positives (FPs) per case. The MTANNs removed 75% of the FPs without loss of any true positives; thus, the performance of our CAD scheme was improved to 4.8 FPs per case at the sensitivity of 71.4% of the polyps "missed" by radiologists.