Deep neural networks have demonstrated their great potential in recent years, exceeding the performance of human experts in a wide range of applications. Due to their large sizes, however, compression techniques such as weight quantization and pruning are usually applied before they can be accommodated on the edge. It is generally believed that quantization leads to performance degradation, and plenty of existing works have explored quantization strategies aiming at minimum accuracy loss. In this paper, we argue that quantization, which essentially imposes regularization on weight representations, can sometimes help to improve accuracy. We conduct comprehensive experiments on three widely used applications: fully connected network for biomedical image segmentation, convolutional neural network for image classification on ImageNet, and recurrent neural network for automatic speech recognition, and experimental results show that quantization can improve the accuracy by 1%, 1.95%, 4.23% on the three applications respectively with 3.5x-6.4x memory reduction.

Congenital heart disease (CHD) is the most common birth defect, accounting for one-third of all congenital birth defects. As with complicated intracardiac structural abnormalities, CHD is usually treated with surgical repair, and computed tomography (CT) is the main examination method for diagnosis of CHD and also provides anatomical information to surgeons. Currently, there exists a serious shortage of professional surgeons in developing countries. Compared with developed countries where large hospitals and cardiovascular disease centers have professional surgical teams with rich treatment experience, surgeons in developing countries and remote areas suffer from lack of professional surgical skills resulting with low surgical quality and high mortality. Recently, surgical telementoring has been popular to tackle the above problems, in which less-skilled surgeons can get real-time guidance from skilled surgeons remotely through audio and video transmission. However, there still exists difficulties in applying telementoring to CHD surgeries including high resource consumption on medical data transmission and storage, large image noise, and inconvenient and inefficient discussion between surgeons on CT. In this article, we proposed a framework with an image compression module, an image denoising module, and an image segmentation module based on CT images in CHD. We evaluated the above three modules and compared them with existing works, respectively, and the results show that our methods achieve much better performance. Furthermore, with 3D printing, VR technology, and 5G communications, our framework was successfully used in a real case study to treat a patient who needed surgical treatment.

As one of the most commonly ordered imaging tests, the computed tomography (CT) scan comes with inevitable radiation exposure that increases cancer risk to patients. However, CT image quality is directly related to radiation dose, and thus it is desirable to obtain high-quality CT images with as little dose as possible. CT image denoising tries to obtain high-dose-like high-quality CT images (domain Y ) from low dose low-quality CT images (domain X ), which can be treated as an image-to-image translation task where the goal is to learn the transform between a source domain X (noisy images) and a target domain Y (clean images). Recently, the cycle-consistent adversarial denoising network (CCADN) has achieved state-of-the-art results by enforcing cycle-consistent loss without the need of paired training data, since the paired data is hard to collect due to patients’ interests and cardiac motion. However, out of concerns on patients’ privacy and data security, protocols typically require clinics to perform medical image processing tasks including CT image denoising locally (i.e., edge denoising). Therefore, the network models need to achieve high performance under various computation resource constraints including memory and performance. Our detailed analysis of CCADN raises a number of interesting questions that point to potential ways to further improve its performance using the same or even fewer computation resources. For example, if the noise is large leading to a significant difference between domain X and domain Y , can we bridge X and Y with a intermediate domain Z such that both the denoising process between X and Z and that between Z and Y are easier to learn? As such intermediate domains lead to multiple cycles, how do we best enforce cycle- consistency? Driven by these questions, we propose a multi-cycle-consistent adversarial network (MCCAN) that builds intermediate domains and enforces both local and global cycle-consistency for edge denoising of CT images. The global cycle-consistency couples all generators together to model the whole denoising process, whereas the local cycle-consistency imposes effective supervision on the process between adjacent domains. Experiments show that both local and global cycle-consistency are important for the success of MCCAN, which outperforms CCADN in terms of denoising quality with slightly less computation resource consumption.