TY  - JOUR
AU  - Gu, Yunrui
AU  - Gao, Zhenzhe
AU  - Kong, Cong
AU  - Du, Jiawei
AU  - Yin, Zhaoxia
PY  - 2026
TI  - Adversarial attacks on medical hyperspectral imaging exploiting spectral-spatial dependencies and multiscale features
JO  - Visual Intelligence
SN  - 2097-3330
SP  - 27
VL  - 4
AB  - Medical hyperspectral imaging (MHSI) has demonstrated considerable promise in disease diagnosis by capturing spectral-spatial information of tissues. While deep learning has substantially improved MHSI classification accuracy, its robustness remains limited due to the well-known trade-off between accuracy and robustness in deep neural networks (DNNs). This issue is particularly critical in MHSI, where reliable prediction depends on local tissue relationships and multiscale spectral-spatial structures. A practical way to improve robustness is to identify the most unstable adversarial examples and incorporate them into adversarial training. However, existing attack methods do not sufficiently exploit these MHSI-specific properties, leading to suboptimal attack effectiveness and limited value for robustness enhancement. To address this gap, we propose a structured adversarial attack framework for MHSI that progressively models its local spectral-spatial dependencies and multiscale hierarchical representations. The proposed method generates anatomically consistent perturbations by modeling neighborhood dependencies and hierarchical spectral-spatial features. Experiments on the hyperspectral human brain image (the Brain) dataset and the multidimensional choledoch (MDC) dataset show that our method degrades lesion-related classification performance in critical tumor regions more effectively than existing baselines while maintaining low perturbation magnitude. These results reveal a clinically relevant robustness weakness in current MHSI models and provide stronger adversarial samples for developing targeted defense strategies.
UR  - https://doi.org/10.1007/s44267-026-00129-x
DO  - 10.1007/s44267-026-00129-x