Can machines be trusted? Robustification of deep learning for medical imaging

机器可以信任吗？

• 批准号: 10208969
• 负责人: John William Garrett
• 金额: $ 31.89万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-02 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10208969
• 关键词: Adopted; Algorithms; Attention; Brain; Cardiac; Classification; Clinical; Clinical Research; Critiques; Dangerous Behavior; Data; Data Set; Diagnostic radiologic examination; Disease; Dose; Effectiveness; Ensure; Exhibits; Exposure to; Failure; Goals; Human; Image; Image Analysis; Label; Machine Learning; Magnetic Resonance Imaging; Mathematics; Medical Imaging; Methods; Modeling; Morphologic artifacts; Motion; Noise; Output; Pattern; Performance; Physics; Positron-Emission Tomography; Predisposition; Recommendation; Research; Research Design; Research Personnel; Scheme; Source; Structure; System; Thoracic Radiography; Training; Trust; Tumor Tissue; Variant; Work; X-Ray Computed Tomography; base; classification algorithm; clinical implementation; clinically relevant; deep learning; deep learning algorithm; design; disease diagnosis; human error; imaging Segmentation; improved; instrument; learning community; loss of function; machine learning algorithm; neural network; novel; operation; performance tests; physical process; radiologist; reconstruction; resilience; statistics; success; tumor

Machine learning algorithms have become increasing popular in medical imaging, where highly functional algorithms have been trained to recognize patterns or features within image data sets and perform clinically relevant tasks such as tumor segmentation and disease diagnosis. In recent years, an approach known as deep learning has revolutionized the field of machine learning, by leveraging massive datasets and immense computing power to extract features from data. Deep learning is ideally suited for problems in medical imaging, and has enjoyed success in diverse tasks such as segmenting cardiac structures, tumors, and tissues. However, research in machine learning has also shown that deep learning is fragile in the sense that carefully designed perturbations to an image can cause the algorithm to fail. These perturbations can be designed to be imperceptible by humans, so that a trained radiologist would not make the same mistakes. As deep learning approaches gain acceptance and move toward clinical implementation, it is therefore crucial to develop a better understanding of the performance of neural networks. Specifically, it is critical to understand the limits of deep learning when presented with noisy or imperfect data. The goal of this project is to explore these questions in the context of medical imaging—to better identify strengths, weaknesses, and failure points of deep learning algorithms. We posit that malicious perturbations, of the type studied in theoretical machine learning, may not be representative of the sort of noise encountered in medical images. Although noise is inevitable in a physical system, the noise arising from sources such as subject motion, operator error, or instrument malfunction may have less deleterious effects on a deep learning algorithm. We propose to characterize the effect of these perturbations on the performance of deep learning algorithms. Furthermore, we will study the effect of random labeling error introduced into the data set, as might arise due to honest human error. We will also develop new methods for making deep learning algorithms more robust to the types of clinically relevant perturbations described above. In summary, although the susceptibility of neural networks to small errors in the inputs is widely recognized in the deep learning community, our work will investigate these general phenomena in the specific case of medical imaging tasks, and also conduct the first study of average-case errors that could realistically arise in clinical studies. Furthermore, we will produce novel recommendations for how to quantify and improve the resiliency of deep learning approaches in medical imaging.

机器学习算法在医学成像领域变得越来越流行，其中功能强大 算法经过训练可以识别图像数据集中的模式或特征并在临床上执行 近年来，一种称为肿瘤分割和疾病诊断的方法。 深度学习利用海量数据集和海量数据，彻底改变了机器学习领域 从数据中提取特征的计算能力非常适合医学成像、 并在分割心脏结构、肿瘤和组织等多种任务中取得了成功。 然而，机器学习的研究也表明深度学习是脆弱的，因为仔细地 对图像设计的扰动可能会导致算法失败。 人类无法察觉，因此训练有素的放射科医生不会犯与深度学习相同的错误。 方法获得接受并走向临床实施，因此至关重要的是开发一种 具体来说，了解神经网络的局限性至关重要。 当呈现嘈杂或不完美的数据时进行深度学习该项目的目标是探索这些。 医学成像背景下的问题——更好地识别医学成像的优势、劣势和失败点 深度学习算法。 我们假设理论机器学习中研究的那种恶意扰动可能并不 尽管噪声在物理中是不可避免的，但它代表了医学图像中遇到的噪声。 系统中，由于主体运动、操作员错误或仪器故障等来源产生的噪声可能会 对深度学习算法的有害影响较小，我们建议描述这些影响的特征。 此外，我们将研究随机因素的影响。 数据集中引入的标签错误可能是由于诚实的人为错误而引起的，我们还将开发新的标签错误。 使深度学习算法对临床相关扰动类型更加鲁棒的方法 如上所述。 总之，尽管神经网络对输入中的小错误的敏感性已被广泛认识到 在深度学习社区中，我们的工作将在特定情况下研究这些普遍现象 医学成像任务，并且还对可能在实际情况中出现的平均情况误差进行了首次研究 此外，我们还将就如何量化和改进临床研究提出新的建议。 医学成像深度学习方法的弹性。