10 citations · 15 across the 5 of their papers we have counts for
6 papers
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…
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…
Contrastive Graph Few-Shot Learning
Chunhui Zhang, Hongfu Liu, Jundong Li +2
Prevailing deep graph learning models often suffer from label sparsity issue. Although many graph few-shot learning (GFL) methods have been developed to avoid performance degradati…
Label-invariant Augmentation for Semi-Supervised Graph Classification
Han Yue, Chunhui Zhang, Chuxu Zhang +1
Recently, contrastiveness-based augmentation surges a new climax in the computer vision domain, where some operations, including rotation, crop, and flip, combined with dedicated a…
Student Network Learning via Evolutionary Knowledge Distillation
Kangkai Zhang, Chunhui Zhang, Shikun Li +2
Knowledge distillation provides an effective way to transfer knowledge via teacher-student learning, where most existing distillation approaches apply a fixed pre-trained model as…
USCL: Pretraining Deep Ultrasound Image Diagnosis Model through Video Contrastive Representation Learning
Yixiong Chen, Chunhui Zhang, Li Liu +4
Most deep neural networks (DNNs) based ultrasound (US) medical image analysis models use pretrained backbones (e.g., ImageNet) for better model generalization. However, the domain…