แนวคิดหลัก
The author proposes a dynamic perturbation-adaptive adversarial training (DPAAT) method to enhance robustness and generalization in medical image classification by dynamically adjusting perturbations based on loss information.
บทคัดย่อ
The paper discusses the challenges of adversarial examples in medical image classification and introduces the DPAAT method to address these issues. By dynamically adapting perturbations and optimizing synchronization between robustness and generalization, the DPAAT shows significant improvements in performance metrics.
The study focuses on dermatology HAM10000 dataset testing, demonstrating superior results of the DPAAT over traditional adversarial training methods. The DPAAT not only enhances robustness but also preserves generalization accuracy while improving interpretability on various CNNs.
Key points include the importance of dynamic perturbation adaptation, synchronization optimization, and the impact on visibility and interpretability in medical image classification tasks. Experimental results show improved robustness, generalization, mean average precision, and mean average robustness precision with the DPAAT method.
สถิติ
Remarkable successes were made in Medical Image Classification (MIC) recently.
Comprehensive testing on dermatology HAM10000 dataset showed that the DPAAT achieved better robustness improvement.
The DPAAT obtained superior interpretability of the CNNs over standard and AT methods.
The average robustness of all six CNNs using the DPAAT was improved under different attack scenarios.
The mAP and mARP of the DPAAT were significantly improved compared to other AT methods.
คำพูด
"The DPAAT not only offered superior robustness and generalization accuracy but also improved interpretability significantly."
"The dynamic perturbation adaptation of the DPAAT alleviated generalization decline while improving robustness."
"The effectiveness of dynamic perturbation adaptation played a crucial role in performance improvements."