From Noisy Labels to Intrinsic Structure: A Geometric-Structural Dual-Guided Framework for Noise-Robust Medical Image Segmentation

1. 1Identify noisy labels in medical image segmentation datasets
2. 2Develop a geometric-structural dual-guided network to learn intrinsic structure from images
3. 3Integrate geometric and structural information to guide feature learning and improve model robustness
4. 4Evaluate the framework's performance on noise-robust medical image segmentation tasks

This research benefits machine learning engineers and medical imaging professionals working on segmentation tasks, as it provides a novel approach to handling noisy labels and improving model performance

💡 Incorporating geometric and structural information can help mitigate the impact of noisy labels on medical image segmentation models

💡 Dual-guided framework for noise-robust medical image segmentation using geometric & structural info