Adversarial Attacks on Medical Segmentation Model via Transformation of Feature Statistics

Deep learning-based segmentation models have made a profound impact on medical procedures, with U-Net based computed tomography (CT) segmentation models exhibiting remarkable performance. Yet, even with these advances, these models are found to be vulnerable to adversarial attacks, a problem that eq...

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Veröffentlicht in:	Applied sciences Jg. 14; H. 6; S. 2576
Hauptverfasser:	Lee, Woonghee, Ju, Mingeon, Sim, Yura, Jung, Young Kul, Kim, Tae Hyung, Kim, Younghoon
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.03.2024
Schlagworte:	adversarial attacks computed tomography (CT) segmentation CT imaging data augmentation Deep learning deep learning-based segmentation Medical research Medical screening Methods realistic adversarial samples Standard deviation Statistics Tomography Germany
ISSN:	2076-3417, 2076-3417
Online-Zugang:	Volltext
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