Medical Image Annotation Based on Deep Transfer Learning

Abstract

The using of deep learning method belongs to the application and research of artificial intelligence technology in medical field for assisting medical image information processing. Due to the larger difference between the image underlying character features and high-level semantic concepts described, the existing image annotation algorithms perform not very ideal, therefore, the we propose an image annotation framework based on multimodule deep transfer learning method, aiming at the application of deep convolution neural network technology to optimize the neural network parameters, and improve the tagging accuracy in detail. The proposed multimodule deep learning annotation framework uses two stages to optimiz neural network parameters: (1) using deep neural network to optimize the single-mode condition parameters; (2) using the phase correlation, to realize the multi-mode state, the experiment of the optimal combination of public data sets shows that the scheme can effectively improve the performance of image annotation.