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...

Celý popis

Podrobná bibliografia

Vydané v:	Applied sciences Ročník 14; číslo 6; s. 2576
Hlavní autori:	Lee, Woonghee, Ju, Mingeon, Sim, Yura, Jung, Young Kul, Kim, Tae Hyung, Kim, Younghoon
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Basel MDPI AG 01.03.2024
Predmet:	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
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!