强大的医疗诊断:用于放射学图像中对抗性疾病检测的新型两阶段深度学习框架

IF 2.9 2区 工程技术 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Journal of Digital Imaging Pub Date : 2024-01-10 DOI:10.1007/s10278-023-00916-8
Sheikh Burhan ul haque, Aasim Zafar
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引用次数: 0

摘要

在医疗诊断领域,深度学习技术的应用,尤其是在放射图像方面的应用,已成为一股变革力量。人工智能(AI),特别是机器学习(ML)和深度学习(DL)的重要意义在于它们能够从放射图像中快速、准确地诊断疾病。在 COVID-19 大流行期间,这种能力尤为重要,快速准确的诊断在控制病毒传播方面发挥了关键作用。在大量放射学图像数据集上训练的 DL 模型在区分正常病例和受 COVID-19 影响的病例方面表现出了非凡的能力,为危机中的人们带来了一线希望。然而,与任何技术进步一样,也会出现漏洞。基于深度学习的诊断模型虽然精通,但也难免受到恶意攻击。这些攻击的特点是对输入数据进行精心设计的扰动,有可能破坏模型的决策过程。在医疗领域,这种漏洞可能会造成严重后果,导致误诊和患者护理受损。为了解决这个问题,我们提出了一个两阶段防御框架,结合了先进的对抗学习和对抗图像过滤技术。在训练阶段,我们使用改进的对抗性学习算法来增强模型对对抗性示例的抵御能力。在推理阶段,我们采用 JPEG 压缩技术来减轻导致误分类的扰动。我们在基于 ResNet-50、VGG-16 和 Inception-V3 的三个模型上评估了我们的方法。这些模型在将肺部区域的放射图像(X 光和 CT)分类为正常、肺炎和 COVID-19 肺炎类别方面表现优异。然后,我们评估了这些模型在三种针对性对抗攻击下的脆弱性:快速梯度符号法 (FGSM)、投射梯度下降法 (PGD) 和基本迭代法 (BIM)。结果表明,受到攻击后,模型性能明显下降。然而,我们的防御框架大大提高了模型抵御对抗性攻击的能力,在对抗性示例上保持了较高的准确性。重要的是,我们的框架确保了模型从干净图像中诊断 COVID-19 的可靠性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Robust Medical Diagnosis: A Novel Two-Phase Deep Learning Framework for Adversarial Proof Disease Detection in Radiology Images

In the realm of medical diagnostics, the utilization of deep learning techniques, notably in the context of radiology images, has emerged as a transformative force. The significance of artificial intelligence (AI), specifically machine learning (ML) and deep learning (DL), lies in their capacity to rapidly and accurately diagnose diseases from radiology images. This capability has been particularly vital during the COVID-19 pandemic, where rapid and precise diagnosis played a pivotal role in managing the spread of the virus. DL models, trained on vast datasets of radiology images, have showcased remarkable proficiency in distinguishing between normal and COVID-19-affected cases, offering a ray of hope amidst the crisis. However, as with any technological advancement, vulnerabilities emerge. Deep learning-based diagnostic models, although proficient, are not immune to adversarial attacks. These attacks, characterized by carefully crafted perturbations to input data, can potentially disrupt the models’ decision-making processes. In the medical context, such vulnerabilities could have dire consequences, leading to misdiagnoses and compromised patient care. To address this, we propose a two-phase defense framework that combines advanced adversarial learning and adversarial image filtering techniques. We use a modified adversarial learning algorithm to enhance the model’s resilience against adversarial examples during the training phase. During the inference phase, we apply JPEG compression to mitigate perturbations that cause misclassification. We evaluate our approach on three models based on ResNet-50, VGG-16, and Inception-V3. These models perform exceptionally in classifying radiology images (X-ray and CT) of lung regions into normal, pneumonia, and COVID-19 pneumonia categories. We then assess the vulnerability of these models to three targeted adversarial attacks: fast gradient sign method (FGSM), projected gradient descent (PGD), and basic iterative method (BIM). The results show a significant drop in model performance after the attacks. However, our defense framework greatly improves the models’ resistance to adversarial attacks, maintaining high accuracy on adversarial examples. Importantly, our framework ensures the reliability of the models in diagnosing COVID-19 from clean images.

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来源期刊
Journal of Digital Imaging
Journal of Digital Imaging 医学-核医学
CiteScore
7.50
自引率
6.80%
发文量
192
审稿时长
6-12 weeks
期刊介绍: The Journal of Digital Imaging (JDI) is the official peer-reviewed journal of the Society for Imaging Informatics in Medicine (SIIM). JDI’s goal is to enhance the exchange of knowledge encompassed by the general topic of Imaging Informatics in Medicine such as research and practice in clinical, engineering, and information technologies and techniques in all medical imaging environments. JDI topics are of interest to researchers, developers, educators, physicians, and imaging informatics professionals. Suggested Topics PACS and component systems; imaging informatics for the enterprise; image-enabled electronic medical records; RIS and HIS; digital image acquisition; image processing; image data compression; 3D, visualization, and multimedia; speech recognition; computer-aided diagnosis; facilities design; imaging vocabularies and ontologies; Transforming the Radiological Interpretation Process (TRIP™); DICOM and other standards; workflow and process modeling and simulation; quality assurance; archive integrity and security; teleradiology; digital mammography; and radiological informatics education.
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