aceAdversarial robustness of deep learning-based thyroid nodule segmentation in ultrasound
Author summary Artificial intelligence systems are increasingly used to assist with medical image analysis, including thyroid nodule segmentation on ultrasound. These systems are typically evaluated using standard test images, yet subtle image changes can alter model behaviour. We examined how a deep learning model for thyroid nodule segmentation responds to deliberate, visually plausible image perturbations introduced under conditions where the attacker cannot access the model’s internal workings. Reference seg…
Simple inference-time denoising restored about 36% of lost segmentation accuracy (Dice +0.10) against the spatial-noise attack, with negligible cost on clean images.
The study used one U-Net model and one single-institution dataset, with 31 test nodules, non-adaptive defenses, a fixed 500-query budget and no reader study of imperceptibility, so generalizability and clinical significance remain unclear.
Evidence
- JournalismPLOS (Public Library of Science)2026-11-04
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Truvace Impact Record TRV-2026-2375, v1: “Adversarial robustness of deep learning-based thyroid nodule segmentation in ultrasound.” Truvace, 2026-11-05. /record/TRV-2026-2375 (accessed at citation time). sha256 3b2761c26bd87e95…
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