用差分隐私保护数据，精准预测胃肠道癌类型
Differentially private fine-tuned NF-Net to predict GI cancer type
- 在NF-Net基础上融合差分隐私优化，保护患者隐私
- 无隐私保护时准确率达88.98%，启用隐私机制后仍保持74.58%以上
- 针对数据不平衡问题设计加权采样与类别权重策略
根据全球基因组状态，胃肠道（GI）肿瘤可分为微卫星不稳定性（MSI）和微卫星稳定性（MSS）。MSI患者适用免疫治疗，而MSS患者则需放疗或化疗。因此，准确区分GI肿瘤的MSI与MSS状态对制定合理治疗方案至关重要。现有研究显示深度学习可直接从组织病理图像预测肿瘤类型，但深度学习模型易受成员推理攻击、模型提取攻击等威胁，限制其在真实场景中的应用。为使深度学习模型既有效又安全，本文将差分隐私（DP）与深度学习结合。具体地，我们对无归一化网络（NF-Net）进行微调，在未启用差分隐私时达到88.98%的准确率；采用DP-AdamW与自适应DP-AdamW后，准确率分别为74.58%和76.48%。此外，我们引入加权随机采样（WRS）和类别权重（CW）缓解数据不平衡问题，并在不同设置下评估了多种差分隐私算法的表现。
原文摘要 · Abstract (English)
Based on global genomic status, the cancer tumor is classified as Microsatellite Instable (MSI) and Microsatellite Stable (MSS). Immunotherapy is used to diagnose MSI, whereas radiation and chemotherapy are used for MSS. Therefore, it is significant to classify a gastro-intestinal (GI) cancer tumor into MSI vs. MSS to provide appropriate treatment. The existing literature showed that deep learning could directly predict the class of GI cancer tumors from histological images. However, deep learning (DL) models are susceptible to various threats, including membership inference attacks, model extraction attacks, etc. These attacks render the use of DL models impractical in real-world scenarios. To make the DL models useful and maintain privacy, we integrate differential privacy (DP) with DL. In particular, this paper aims to predict the state of GI cancer while preserving the privacy of sensitive data. We fine-tuned the Normalizer Free Net (NF-Net) model. We obtained an accuracy of 88.98\% without DP to predict (GI) cancer status. When we fine-tuned the NF-Net using DP-AdamW and adaptive DP-AdamW, we got accuracies of 74.58% and 76.48%, respectively. Moreover, we investigate the Weighted Random Sampler (WRS) and Class weighting (CW) to solve the data imbalance. We also evaluated and analyzed the DP algorithms in different settings.
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