7 citations · 7 across the 5 of their papers we have counts for
6 papers · 1 filter
Beyond Low-Rank Tuning: Model Prior-Guided Rank Allocation for Effective Transfer in Low-Data and Large-Gap Regimes
Chuyan Zhang, Kefan Wang, Yun Gu
Low-Rank Adaptation (LoRA) has proven effective in reducing computational costs while maintaining performance comparable to fully fine-tuned foundation models across various tasks.…
PASS:Test-Time Prompting to Adapt Styles and Semantic Shapes in Medical Image Segmentation
Chuyan Zhang, Hao Zheng, Xin You +2
Test-time adaptation (TTA) has emerged as a promising paradigm to handle the domain shifts at test time for medical images from different institutions without using extra training…
Reflecting Topology Consistency and Abnormality via Learnable Attentions for Airway Labeling
Chenyu Li, Minghui Zhang, Chuyan Zhang +1
Accurate airway anatomical labeling is crucial for clinicians to identify and navigate complex bronchial structures during bronchoscopy. Automatic airway anatomical labeling is cha…
RESTORE: Towards Feature Shift for Vision-Language Prompt Learning
Yuncheng Yang, Chuyan Zhang, Zuopeng Yang +6
Prompt learning is effective for fine-tuning foundation models to improve their generalization across a variety of downstream tasks. However, the prompts that are independently opt…
AC-Norm: Effective Tuning for Medical Image Analysis via Affine Collaborative Normalization
Chuyan Zhang, Yuncheng Yang, Hao Zheng +1
Driven by the latest trend towards self-supervised learning (SSL), the paradigm of "pretraining-then-finetuning" has been extensively explored to enhance the performance of clinica…
Dive into Self-Supervised Learning for Medical Image Analysis: Data, Models and Tasks
Chuyan Zhang, Yun Gu
Self-supervised learning (SSL) has achieved remarkable performance in various medical imaging tasks by dint of priors from massive unlabelled data. However, regarding a specific do…