Search Results for author:
Found 5 papers, 4 papers with code
Airway Labeling Meets Clinical Applications: Reflecting Topology Consistency and Outliers via Learnable Attentions
This paper aims to address these challenges by proposing a novel method that enhances topological consistency and improves the detection of abnormal airway branches.
PASS:Test-Time Prompting to Adapt Styles and Semantic Shapes in Medical Image Segmentation
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 data.
RESTORE: Towards Feature Shift for Vision-Language Prompt Learning
Prompt learning is effective for fine-tuning foundation models to improve their generalization across a variety of downstream tasks.
AC-Norm: Effective Tuning for Medical Image Analysis via Affine Collaborative Normalization
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 clinical applications with limited annotations.
Dive into Self-Supervised Learning for Medical Image Analysis: Data, Models and Tasks
Self-supervised learning (SSL) has achieved remarkable performance in various medical imaging tasks by dint of priors from massive unlabelled data.
