1 citations · 1 across the 4 of their papers we have counts for
5 papers
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…
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…
Beyond Skip Connection: Pooling and Unpooling Design for Elimination Singularities
Chengkun Sun, Jinqian Pan, Zhuoli Jin +3
Training deep Convolutional Neural Networks (CNNs) presents unique challenges, including the pervasive issue of elimination singularities, consistent deactivation of nodes leading…
Gaze-directed Vision GNN for Mitigating Shortcut Learning in Medical Image
Shaoxuan Wu, Xiao Zhang, Bin Wang +3
Deep neural networks have demonstrated remarkable performance in medical image analysis. However, its susceptibility to spurious correlations due to shortcut learning raises concer…