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InfoResearchPeer-reviewed
Exploring lightweight knowledge distillation with adversarial attack for efficient coronary artery vessel segmentation
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Summary
Researchers propose KDLight, a lightweight knowledge distillation framework with an adversarial attack module for coronary artery vessel segmentation. The adversarial attacker, placed before the generator, produces perturbations during training to improve generalization and reduce overfitting. Tested on two datasets, the method reportedly outperforms state-of-the-art approaches, with p-values below 0.05, while using markedly lower FLOPs and parameter counts.