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From Spatial Semantics to Temporal Context: Leveraging Gaze Trajectory for Weakly Supervised Medical Image Segmentation
Shaoxuan Wu, Xiao Zhang, Xiaodi Zhao +3
Medical image segmentation heavily depends on labor-intensive and time-consuming pixel-level annotations. Eye tracking offers a cost-effective solution that can be naturally integr…
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…
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…