output
20172026
most citedLow Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss

1.7k citations

Showing 2020 · eess.IVShow all

5 papers · 2 filters

eess.IV2020★ 31 cited

Conditional Generation of Medical Images via Disentangled Adversarial Inference

Mohammad Havaei, Ximeng Mao, Yiping Wang +1

Synthetic medical image generation has a huge potential for improving healthcare through many applications, from data augmentation for training machine learning systems to preservi…

eess.IV2020★ 6 cited

Blind deblurring for microscopic pathology images using deep learning networks

Cheng Jiang, Jun Liao, Pei Dong +6

Artificial Intelligence (AI)-powered pathology is a revolutionary step in the world of digital pathology and shows great promise to increase both diagnosis accuracy and efficiency.…

eess.IV2020★ 68 cited

Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT Imaging

Rajesh Kumar, Abdullah Aman Khan, Sinmin Zhang +7

With the increase of COVID-19 cases worldwide, an effective way is required to diagnose COVID-19 patients. The primary problem in diagnosing COVID-19 patients is the shortage and r…

eess.IV2020★ 78 cited

Joint Prediction and Time Estimation of COVID-19 Developing Severe Symptoms using Chest CT Scan

Xiaofeng Zhu, Bin Song, Feng Shi +9

With the rapidly worldwide spread of Coronavirus disease (COVID-19), it is of great importance to conduct early diagnosis of COVID-19 and predict the time that patients might conve…

eess.IV2020★ 246 cited

Large-Scale Screening of COVID-19 from Community Acquired Pneumonia using Infection Size-Aware Classification

Feng Shi, Liming Xia, Fei Shan +7

The worldwide spread of coronavirus disease (COVID-19) has become a threatening risk for global public health. It is of great importance to rapidly and accurately screen patients w…