Progressive Transmission of Medical Images via a Bank of Generative Adversarial Networks.

Ching-Chun Chang,Xu Wang,Ji-Hwei Horng,Isao Echizen,Chi-Hua Chen

doi:10.1155/2021/9917545

Abstract

The healthcare sector is currently undergoing a major transformation due to the recent advances in deep learning and artificial intelligence. Despite a significant breakthrough in medical imaging and diagnosis, there are still many open issues and undeveloped applications in the healthcare domain. In particular, transmission of a large volume of medical images proves to be a challenging and time-consuming problem, and yet no prior studies have investigated the use of deep neural networks towards this task. The purpose of this paper is to introduce and develop a deep-learning approach for the efficient transmission of medical images, with a particular interest in the progressive coding of bit-planes. We establish a connection between bit-plane synthesis and image-to-image translation and propose a two-step pipeline for progressive image transmission. First, a bank of generative adversarial networks is trained for predicting bit-planes in a top-down manner, and then prediction residuals are encoded with a tailored adaptive lossless compression algorithm. Experimental results validate the effectiveness of the network bank for generating an accurate low-order bit-plane from high-order bit-planes and demonstrate an advantage of the tailored compression algorithm over conventional arithmetic coding for this special type of prediction residuals in terms of compression ratio.

Highlights

With the development of digital imaging equipment, more and more medical images have been produced and a medical image dataset usually contains a large number of images
In [2], a unique pair of interfaces was proposed to provide a fast thoroughfare for medical data transmission between two DICOM applications and could speed up 1.22 and 13 times, respectively, in the local area networks (LAN) and wide area networks (WAN)
We evaluate the performance of our proposed bit-plane prediction scheme with generative adversarial networks and compare our proposed residual bit-plane compression scheme with the conventional arithmetic coding compression technique. e dataset used for training and testing, the training details, and the evaluation metrics are first introduced, and the performance of the proposed scheme is evaluated

Summary

Introduction

With the development of digital imaging equipment, more and more medical images have been produced and a medical image dataset usually contains a large number of images. Based on the GAN network, a powerful architecture pix2pix [37] was set up as an image bit-plane predictor to translate the image styles and has been adopted in different scenarios such as steganography [38] and even in medical images [39, 40] These deep learning techniques have not been applied to the medical image transmission. (1) Introduce a deep neural network of pix2pix GAN network into the research of progressive medical image prediction and transmission (2) Design a GAN-based predictor bank to help bitplane compression of medical images (3) Propose an adaptive residual bit-plane compression technique of high compression ratio.

Bit-Plane Method of PIT

Progressive Bit-Plane Prediction and Compression

Experimental Results

Conclusions