A self-supervised feature-standardization-block for cross-domain lung disease classification.

With the advance of deep learning technology, convolutional neural network (CNN) has been wildly used and achieved the state-of-the-art performances in the area of medical image classification. However, most existing medical image classification methods conduct their experiments on only one public dataset. When applying a well-trained model to a different dataset selected from different sources, the model usually shows large performance degradation and needs to be fine-tuned before it can be applied to the new dataset. The goal of this work is trying to solve the cross-domain image classification problem without using data from target domain. In this work, we designed a self-supervised plug-and-play feature-standardization-block (FSB) which consisting of image normalization (INB), contrast enhancement (CEB) and boundary detection blocks (BDB), to extract cross-domain robust feature maps for deep learning framework, and applied the network for chest x-ray-based lung diseases classification. Three classic deep networks, i.e. VGG, Xception and DenseNet and four chest x-ray lung diseases datasets were employed for evaluating the performance. The experimental result showed that when employing feature-standardization-block, all three networks showed better domain adaption performance. The image normalization, contrast enhancement and boundary detection blocks achieved in average 2%, 2% and 5% accuracy improvement, respectively. By combining all three blocks, feature-standardization-block achieved in average 6% accuracy improvement.

A smartphone-based online tool for prehospital self-triage of COVID-19.

The COVID-19 epidemic has swept across China and spread to other countries. The rapid spreading of COVID-19 and panic combined with the lack of a hierarchical medical system in China have resulted in a huge number of hospital visiting which are overwhelming local medical system and increasing the incidence of cross infection. To meliorate this situation, we adopted the management concept of the system of Tiered Diagnosis and Treatment and developed an online tool for self-triage based on the mostly used multi-purpose smartphone app Wechat in China. This online tool helps people perform self-triage so that they can decide whether to quarantine at home or visit hospital. This tool further provides instructions for home quarantine and help patients make an appointment online if hospital visiting suggested. This smartphone application can reduce the burden on hospitals without losing the truly COVID-19 patients and protect people from the danger of cross infection.

Infection Control against COVID-19 in Departments of Radiology.

The COVID-19 epidemic, which is caused by the novel coronavirus SARS-CoV-2, has spread rapidly to become a world-wide pandemic. Chest radiography and chest CT are frequently used to support the diagnosis of COVID-19 infection. However, multiple cases of COVID-19 transmission in radiology department have been reported. Here we summarize the lessons we learned and provide suggestions to improve the infection control and prevention practices of healthcare workers in departments of radiology.

Corona Virus International Public Health Emergencies: Implications for Radiology Management.

The outbreak of 2019 novel coronavirus (2019-nCoV) pneumonia was reported in Wuhan, Hubei Province, China in December 2019 and has spread internationally. This article discusses how radiology departments can most effectively respond to this public health emergency.

CT Imaging and Differential Diagnosis of COVID-19.

Since the beginning of 2020, coronavirus disease 2019 (COVID-19) has spread throughout China. This study explains the findings from lung computed tomography images of some patients with COVID-19 treated in this medical institution and discusses the difference between COVID-19 and other lung diseases.