Enhancing the robustness of deep learning models against adversarial attacks is crucial, especially in critical domains like healthcare where significant financial interests heighten the risk of such attacks. Whole slide images (WSIs) are high-resolution, digitized versions of tissue samples mounted on glass slides, scanned using sophisticated imaging equipment. The digital analysis of WSIs presents unique challenges due to their gigapixel size and multi-resolution storage format. In this work, we aim at improving the robustness of cancer Gleason grading classification systems against adversarial attacks, addressing challenges at both the image and graph levels. As regards the proposed algorithm, we develop a novel and innovative graph-based model which utilizes GNN to extract features from the graph representation of WSIs. A denoising module, along with a pooling layer is incorporated to manage the impact of adversarial attacks on the WSIs. The process concludes with a transformer module that classifies various grades of prostate cancer based on the processed data. To assess the effectiveness of the proposed method, we conducted a comparative analysis using two scenarios. Initially, we trained and tested the model without the denoiser using WSIs that had not been exposed to any attack. We then introduced a range of attacks at either the image or graph level and processed them through the proposed network. The performance of the model was evaluated in terms of accuracy and kappa scores. The results from this comparison showed a significant improvement in cancer diagnosis accuracy, highlighting the robustness and efficiency of the proposed method in handling adversarial challenges in the context of medical imaging.

翻译：增强深度学习模型对对抗攻击的鲁棒性至关重要，尤其在医疗等关键领域，巨大的经济利益加剧了此类攻击的风险。全切片图像（whole slide images, WSIs）是利用精密成像设备扫描玻璃载玻片上的组织样本生成的高分辨率数字化图像。由于WSI具有千兆像素尺寸和多分辨率存储格式，其数字分析面临独特挑战。本研究旨在提升癌症Gleason分级分类系统对对抗攻击的鲁棒性，同时应对图像层面和图层面的挑战。针对所提算法，我们开发了一种新颖的创新性图模型，利用图神经网络（GNN）从WSI的图表示中提取特征。通过集成去噪模块与池化层来管理对抗攻击对WSI的影响。最后采用Transformer模块基于处理后的数据对前列腺癌进行多级分类。为评估所提方法的有效性，我们设计了两种场景进行对比分析：首先使用未受攻击的WSI训练并测试不含去噪器的模型，随后在图像或图层面引入多种攻击，通过所提网络进行处理。模型性能通过准确率和Kappa值进行评估，对比结果显示癌症诊断准确率显著提升，验证了该方法在处理医学影像对抗攻击挑战中的鲁棒性和高效性。