1 citations · 1 across the 2 of their papers we have counts for
5 papers
MedIQA: A Scalable Foundation Model for Prompt-Driven Medical Image Quality Assessment
Siyi Xun, Yue Sun, Jingkun Chen +5
Rapid advances in medical imaging technology underscore the critical need for precise and automated image quality assessment (IQA) to ensure diagnostic accuracy. Existing medical I…
PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation
Xiao Zhang, Zhuo Jin, Shaoxuan Wu +6
Accurate vessel segmentation is crucial to assist in clinical diagnosis by medical experts. However, the intricate tree-like tubular structure of blood vessels poses significant ch…
TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation
Peilin Zhang, Shaouxan Wua, Jun Feng +5
Background and objective: Medical image segmentation is a core task in various clinical applications. However, acquiring large-scale, fully annotated medical image datasets is both…
From Gaze to Insight: Bridging Human Visual Attention and Vision Language Model Explanation for Weakly-Supervised Medical Image Segmentation
Jingkun Chen, Haoran Duan, Xiao Zhang +3
Medical image segmentation remains challenging due to the high cost of pixel-level annotations for training. In the context of weak supervision, clinician gaze data captures region…
HELPNet: Hierarchical Perturbations Consistency and Entropy-guided Ensemble for Scribble Supervised Medical Image Segmentation
Xiao Zhang, Shaoxuan Wu, Peilin Zhang +5
Creating fully annotated labels for medical image segmentation is prohibitively time-intensive and costly, emphasizing the necessity for innovative approaches that minimize relianc…