General Electric Company
CLASSIFICATION BASED ON ANNOTATION INFORMATION

Abstract:

Systems and techniques for classification based on annotation information are presented. In one example, a system trains a convolutional neural network based on training data and a plurality of images. The training data is associated with a plurality of patients from at least one imaging device. The plurality of images is associated with a plurality of masks from a plurality of objects. The system also generates a first loss function based on the plurality of masks, a second loss function based on a plurality of image level labels associated with the plurality of images, and a third loss function based on the first loss function and the second loss function, where the third loss function is iteratively back propagated to tune parameters of the convolutional neural network. The system also predicts a classification label for an input image based on the convolutional neural network.