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This paper introduces a novel evaluation method for deep learning models in medical imaging, focusing on their robustness against adversarial examples—images that can deceive models while appearing nearly identical to original data. Through experiments on skin lesion classification and whole brain segmentation, the authors demonstrate that models with similar generalizability can exhibit significant differences in robustness when faced with adversarial inputs. The study highlights the importance of evaluati

Author

udaydebnath999999

Language

EN