Mixture of expert artificial neural networks with ensemble training for reduction of various sources of false positives in CAD

Our purpose was to reduce false-positive (FP) detections generated by a computerized lesion detection scheme by using a "mixture of expert" massive-training artificial neural networks (MTANNs). Multiple MTANNs were trained with "ensemble training" for reduction of diverse types of non-lesions. We started from a seed MTANN trained with lesions and non-lesions of a seed type. We applied the trained seed MTANN to lesions and various types of non-lesions to analyze the weakness of the seed MTANN. We arranged the output scores of the MTANN for lesions and non-lesions in an ascending order to form the score scale representing the degree of difficulty in distinction between lesions and non-lesions by the seed MTANN. The score scale was divided into several segments, and ten non-lesions were sampled from the center of each segment so that sets of non-lesion samples covered diverse difficulties. We trained several MTANNs with several sets of non-lesions so that each MTANN became an expert for the non-lesions at a certain level of difficulty. We then combined expert MTANNs with a mixing ANN to form a "mixture of expert" MTANNs. Our database consisted of CT colonography datasets acquired from 100 patients, including 26 polyps. We applied our initial CAD scheme to this CTC database. FP sources included haustral folds, stool, colonic walls, the ileocecal valves, and rectal tubes. The mixture of expert MTANNs distinguished all polyps correctly from more than 50% of the non-polyps. Thus, the mixture of expert MTANNs was able reduce one half of the FPs generated by a computerized polyp detection scheme while the original sensitivity was maintained. We compare the effectiveness of ensemble training with that of training with manually selected cases. The performance of the MTANNs with ensemble training was superior to that of the MTANNs trained with manually selected cases.