302 citations · 898 across the 10 of their papers we have counts for
10 papers
DLTTA: Dynamic Learning Rate for Test-time Adaptation on Cross-domain Medical Images
Hongzheng Yang, Cheng Chen, Meirui Jiang +4
Test-time adaptation (TTA) has increasingly been an important topic to efficiently tackle the cross-domain distribution shift at test time for medical images from different institu…
Source-Free Domain Adaptive Fundus Image Segmentation with Denoised Pseudo-Labeling
Cheng Chen, Quande Liu, Yueming Jin +2
Domain adaptation typically requires to access source domain data to utilize their distribution information for domain alignment with the target data. However, in many real-world s…
Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching
Quande Liu, Hongzheng Yang, Qi Dou +1
Federated learning (FL) has emerged with increasing popularity to collaborate distributed medical institutions for training deep networks. However, despite existing FL algorithms o…
FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space
Quande Liu, Cheng Chen, Jing Qin +2
Federated learning allows distributed medical institutions to collaboratively learn a shared prediction model with privacy protection. While at clinical deployment, the models trai…
Contrastive Cross-site Learning with Redesigned Net for COVID-19 CT Classification
Zhao Wang, Quande Liu, Qi Dou
The pandemic of coronavirus disease 2019 (COVID-19) has lead to a global public health crisis spreading hundreds of countries. With the continuous growth of new infections, develop…
Shape-aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domains
Quande Liu, Qi Dou, Pheng-Ann Heng
Model generalization capacity at domain shift (e.g., various imaging protocols and scanners) is crucial for deep learning methods in real-world clinical deployment. This paper tack…