22 citations · 63 across the 11 of their papers we have counts for
5 papers · 1 filter
Generating and Weighting Semantically Consistent Sample Pairs for Ultrasound Contrastive Learning
Yixiong Chen, Chunhui Zhang, Chris H. Q. Ding +1
Well-annotated medical datasets enable deep neural networks (DNNs) to gain strong power in extracting lesion-related features. Building such large and well-designed medical dataset…
Rethinking Two Consensuses of the Transferability in Deep Learning
Yixiong Chen, Jingxian Li, Chris Ding +1
Deep transfer learning (DTL) has formed a long-term quest toward enabling deep neural networks (DNNs) to reuse historical experiences as efficiently as humans. This ability is name…
Synthetic Tumors Make AI Segment Tumors Better
Qixin Hu, Junfei Xiao, Yixiong Chen +4
We develop a novel strategy to generate synthetic tumors. Unlike existing works, the tumors generated by our strategy have two intriguing advantages: (1) realistic in shape and tex…
HiCo: Hierarchical Contrastive Learning for Ultrasound Video Model Pretraining
Chunhui Zhang, Yixiong Chen, Li Liu +2
The self-supervised ultrasound (US) video model pretraining can use a small amount of labeled data to achieve one of the most promising results on US diagnosis. However, it does no…
MetaLR: Meta-tuning of Learning Rates for Transfer Learning in Medical Imaging
Yixiong Chen, Li Liu, Jingxian Li +3
In medical image analysis, transfer learning is a powerful method for deep neural networks (DNNs) to generalize well on limited medical data. Prior efforts have focused on developi…