Resilience of UNet-Based Models Under Adversarial Conditions in Medical Image Segmentation †
Koishiyeva D. Kang J.W. Iliev T. Bissembayev A. Mukasheva A.
2025Multidisciplinary Digital Publishing Institute (MDPI)
Engineering Proceedings
2025#104Issue 1
Adversarial modifications of input data can degrade the stability of deep neural networks in medical image segmentation. This study evaluates the robustness of UNet and Att-UNet++ architectures using the NuInsSeg dataset with annotated nuclear regions from various tissue sources. Both models were trained and tested under eight perturbation types, including gradient-based, iterative, and stochastic methods, with identical parameter settings. In the absence of distortions, Att-UNet++ produced higher segmentation results with a Dice of 0.7160 and a mean IoU of 0.6190 compared to 0.6424 and 0.4732 for UNet. Under NI-FGSM and Gaussian noise, Att-UNet++ experienced a greater reduction in mean IoU, reaching 0.1215 and 0.0658, while UNet maintained 0.1968 and 0.2329. Loss landscape analysis showed smoother surfaces for Att-UNet++, yet revealed increased responsiveness to directional gradients. The findings suggest that improvements in segmentation accuracy through architectural modifications may be accompanied by increased vulnerability to input changes, highlighting the necessity of robustness evaluation in model development for medical image analysis.
adversarial attack , attention , deep learning , medical image analysis , segmentation
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School of Information Technology and Engineering, Kazakh-British Technical University, Almaty, 050000, Kazakhstan
Department of Transportation System Engineering, Korea National University of Transportation, Uiwang-Si, 27469, South Korea
Department of Telecommunication, University of Ruse, Ruse, 7004, Bulgaria
School of Information Technology and Engineering
Department of Transportation System Engineering
Department of Telecommunication
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026