Machine learning algorithms to analyze large medical image datasets

用于分析大型医学图像数据集的机器学习算法

• 批准号: 10182522
• 负责人: SIMON K WARFIELD
• 金额: $ 36.96万
• 依托单位: BOSTON CHILDREN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10182522
• 关键词: Active Learning; Address; Adoption; Algorithms; Benchmarking; Brain; Characteristics; Child; Clinical; Collection; Crowding; Data; Data Collection; Data Set; Databases; Development; Diagnosis; Fatigue; Image; Image Analysis; Informatics; Interruption; Label; Learning; Life; Machine Learning; Medical Imaging; Methods; Modeling; Morphologic artifacts; Noise; Patient Care; Patient-Focused Outcomes; Patients; Performance; Reference Standards; Research; Sampling; Speed; Structure; Techniques; Training; Uncertainty; Update; autism spectrum disorder; base; clinical decision-making; cost; crowdsourcing; deep learning; design; experimental study; imaging Segmentation; imaging biomarker; improved; innovation; insight; learning algorithm; learning strategy; machine learning algorithm; novel; novel strategies; prevent; quantitative imaging; radiologist; success; supervised learning

Machine learning (ML) is poised to enable faster and more accurate interpretation of medical images by augmenting the capabilities of experts. The cost and difficulty of generating expert quality labelled image data is the primary limitation preventing faster progress and deployment in more domains. Success of ML techniques for medical image interpretation may reduce the burden on radiologists, reducing errors arising from fatigue or interruption, while simultaneously reducing costs and increasing speed and accuracy for patients. Our overall objective for this research is to dramatically reduce the burden of creating high quality reference labels by requiring only a small set of such labels from experts. We propose to address this problem by creating innovative algorithms that will construct reference quality labelled data with little input from domain experts, thus dramatically reducing the cost of labelling. This will enable us to apply ML techniques to generate high quality labels of the large amounts of unlabeled data that are already available, which in turn will facilitate the assessment of potential quantitative imaging biomarkers. We will develop, extend and evaluate novel algorithms that represent three distinct strategies for reducing labelling cost. These three strategies are learning from unlabelled data incorporating a novel strategy for characterizing uncertainty, optimizing sample selection for expert quality labelling with a novel form of Active Learning especially suited for deep learning, and reducing the cost of achieving quality labeling by replacing or augmenting an expert with a crowd of inexperts. We will then implement and distribute these novel algorithms, facilitating the replication of our experiments. Finally, we will demonstrate the practical efficacy of these three strategies by applying them to the important challenge of identifying quantitative imaging biomarkers that best capture alterations in brain structure that are associated with characteristics of ASD. These fundamental advances in informatics algorithms will reduce the cost and increase the rate of obtaining quality labels, which will in turn facilitate the widespread adoption and deployment of machine learning algorithms for image interpretation. Ultimately, this will stimulate the development of new imaging biomarkers that hold the potential to dramatically improve clinical decision-making and patient outcomes.

机器学习（ML）有望通过 增强专家的能力。产生专家质量标记图像数据的成本和困难 是阻止更快进步和部署更多域中的主要限制。 ML的成功 医学图像解释的技术可能会减轻放射科医生的负担，从而减少错误 从疲劳或中断，同时降低成本，并提高速度和准确性 患者。这项研究的总体目标是大大减轻创造高质量的负担 参考标签仅需要专家的一小部分此类标签。我们建议解决这个问题 通过创建创新算法，该算法将构建参考质量标记的数据，而域中的输入很少 专家，因此大大降低了标签成本。这将使我们能够应用ML技术生成 大量未标记数据的高质量标签，这反过来将有助于 评估潜在的定量成像生物标志物。我们将开发，扩展和评估新颖 代表降低标签成本的三种不同策略的算法。这三个策略是 从未标记的数据中学习，该数据包含了一种表征不确定性的新型策略，优化样本 选择专家质量标签，采用一种新颖的活跃学习形式，特别适合深度学习， 并通过替换或增加一群人来降低获得质量标签的成本 熟版。然后，我们将实施和分发这些新颖的算法，从而促进我们的复制 实验。最后，我们将通过将这三种策略应用于 确定最能捕获大脑改变的定量成像生物标志物的重要挑战 与ASD特征相关的结构。这些信息学的基本进步 算法将降低成本并提高获得质量标签的速度，这反过来又有助于 用于图像解释的机器学习算法的广泛采用和部署。最终，这个 将刺激具有显着改善潜力的新成像生物标志物的发展 临床决策和患者结果。