基于多损失混合对抗函数和启发式投影算法的逼真医学图像增强方法  

Realistic Medical Image Augmentation by Using Multi⁃loss Hybrid Adversarial Function and Heuristic Projection Algorithm

作　　者：王见 成楚凡 陈芳 WANG Jian;CHENG Chufan;CHEN Fang(College of Computer Science and Technology,Nanjing University of Aeronautics&Astronautics,Nanjing 211106,China)

机构地区：[1]南京航空航天大学计算机科学与技术学院,南京211106

出　　处：《数据采集与处理》2023年第5期1104-1111,共8页Journal of Data Acquisition and Processing

摘　　要：早期发现新冠肺炎可以及时医疗干预提高患者的存活率,而利用深度神经网络(Deep neural networks,DNN)对新冠肺炎进行检测,可以提高胸部CT对其筛查的敏感性和判读速度。然而,DNN在医学领域的应用受到有限样本和不可察觉的噪声扰动的影响。本文提出了一种多损失混合对抗方法来搜索含有可能欺骗网络的有效对抗样本,将这些对抗样本添加到训练数据中,以提高网络对意外噪声扰动的稳健性和泛化能力。特别是,本文方法不仅包含了风格、原图和细节损失在内的多损失功能从而将医学对抗样本制作成逼真的样式,而且使用启发式投影算法产生具有强聚集性和干扰性的噪声。这些样本被证明具有较强的抗去噪能力和攻击迁移性。在新冠肺炎数据集上的测试结果表明,基于该算法的对抗攻击增强后的网络诊断正确率提高了4.75%。因此,基于多损失混合和启发式投影算法的对抗攻击的增强网络能够提高模型的建模能力,并具有抗噪声扰动的能力。Early detection of COVID-19 allows medical intervention to improve the survival rate of patients.The use of deep neural networks(DNN)to detect COVID-19 can improve the sensitivity and speed of interpretation of chest CT for COVID-19 screening.However,applying DNN for the medical field is known to be influenced by the limited samples and imperceptible noise perturbations.In this paper,we propose a multi-loss hybrid adversarial function(MLAdv)to search the effective adversarial attack samples containing potential spoofing networks.These adversarial attack samples are then added to the training data to improve the robustness and the generalization of the network for unanticipated noise perturbations.Especially,MLAdv not only implements the multiple-loss function including style,origin,and detail losses to craft medical adversarial samples into realistic-looking styles,but also uses the heuristic projection algorithm to produce the noise with strong aggregation and interference.These samples are proven to have stronger anti-noise ability and attack transferability.By evaluating on COVID-19 dataset,it is shown that the augmented networks by using adversarial attacks from the MLAdv algorithm can improve the diagnosis accuracy by 4.75%.Therefore,the augmented network based on MLAdv adversarial attacks can improve the ability of models and is resistant to noise perturbations.

关 键 词：医学图像增强 对抗性攻击 多损失混合 启发式投影法 攻击迁移性 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]