Massive-Training Artificial Neural Network Deep Learning in Computer-Aided Diagnosis for Chest and Abdomen

Abstract

In this paper, a brief overview of massive-training artificial neural network (MTANN) deep learning and its applications are described. The MTANN deep learning is a class of neural networks which directly learn and output images, whereas other deep learning generally outputs classes. The input to the neural network is pixel values in a local region (image patch) in an input image, whereas the output is a single pixel value. The entire output image is obtained by scanning the neural network with the local window (region) in a convolutional manner. In a training stage, a distribution map of the likelihood of being a lesion is given as a teaching image for the MTANN. For example, to classify between lung nodules and non-nodules, a Gaussian distribution, its peak of which is located at the center of a nodule, is given for a positive (i.e., nodule) sample; and value zeros for a negative (i.e., non-nodule) sample. The authors introduce the applications of the MTANN deep learning to false-positive reduction in computer-aided detection of non-polypoid (“flat”) lesions in CT colonography, differential diagnosis of lung nodules in chest CT, and classification of diffuse lung diseases in chest CT.