185 citations · 269 across the 13 of their papers we have counts for
14 papers
Modality Bank: Learn multi-modality images across data centers without sharing medical data
Qi Chang, Hui Qu, Zhennan Yan +3
Multi-modality images have been widely used and provide comprehensive information for medical image analysis. However, acquiring all modalities among all institutes is costly and o…
Object-Guided Instance Segmentation With Auxiliary Feature Refinement for Biological Images
Jingru Yi, Pengxiang Wu, Hui Tang +7
Instance segmentation is of great importance for many biological applications, such as study of neural cell interactions, plant phenotyping, and quantitatively measuring how cells…
Liver Fibrosis and NAS scoring from CT images using self-supervised learning and texture encoding
Ananya Jana, Hui Qu, Carlos D. Minacapelli +3
Non-alcoholic fatty liver disease (NAFLD) is one of the most common causes of chronic liver diseases (CLD) which can progress to liver cancer. The severity and treatment of NAFLD i…
Training Federated GANs with Theoretical Guarantees: A Universal Aggregation Approach
Yikai Zhang, Hui Qu, Qi Chang +3
Recently, Generative Adversarial Networks (GANs) have demonstrated their potential in federated learning, i.e., learning a centralized model from data privately hosted by multiple…
Multi-modal AsynDGAN: Learn From Distributed Medical Image Data without Sharing Private Information
Qi Chang, Zhennan Yan, Lohendran Baskaran +5
As deep learning technologies advance, increasingly more data is necessary to generate general and robust models for various tasks. In the medical domain, however, large-scale and…
Deep Learning based NAS Score and Fibrosis Stage Prediction from CT and Pathology Data
Ananya Jana, Hui Qu, Puru Rattan +3
Non-Alcoholic Fatty Liver Disease (NAFLD) is becoming increasingly prevalent in the world population. Without diagnosis at the right time, NAFLD can lead to non-alcoholic steatohep…