Core Concepts
UMed method enhances protection of medical image segmentation datasets by injecting contour- and texture-aware perturbations.
Abstract
The content discusses the challenges of unauthorized training on medical image segmentation (MIS) datasets and introduces the UMed method to protect these datasets. It highlights the importance of prior knowledge in MIS, such as contours and textures, in generating imperceptible perturbations to prevent unauthorized model training. The method's effectiveness, transferability, invisibility, robustness against defenses, and ablation study are thoroughly discussed.
Directory:
Introduction
Importance of medical images in healthcare.
Concerns about unauthorized AI model training.
Unlearnable Examples (UEs)
Methods for protecting images from unauthorized usage.
UMed Method
Proposal of UMed for MIS dataset protection.
Integration of contour- and texture-aware perturbations.
Experimental Results
Evaluation of UMed's protective capability, transferability, invisibility, and robustness against defenses.
Ablation Study
Impact of contour and texture perturbations on protection performance.
Conclusion
Summary of UMed's effectiveness in safeguarding MIS datasets.
Stats
Recently Unlearnable Examples (UEs) methods have shown potential to protect images by adding invisible shortcuts.
Average PSNR is 50.03 with protective performance degrading clean average DSC from 82.18% to 6.80%.
Protection performance comparison on BUSI dataset: UMed achieves Jac. 0.46% and DSC 0.92%.
Quotes
"The widespread availability of publicly accessible medical images has significantly propelled advancements in various research and clinical fields."
"UMed integrates the prior knowledge of MIS by injecting contour- and texture-aware perturbations to protect images."