Machine learning algorithms to analyze large medical image datasets

机器学习(ML)有望通过以下方式实现更快、更准确的医学图像解释 增强专家的能力。生成专家质量标记图像数据的成本和难度 是阻碍更多领域更快进展和部署的主要限制。ML的成功 医学图像解释技术可以减轻放射科医生的负担，减少产生的错误 防止疲劳或中断，同时降低成本并提高速度和准确性 病人。我们这项研究的总体目标是大幅减轻创造高质量产品的负担 通过只需要来自专家的一小部分这样的标签来引用标签。我们建议解决这个问题 通过创建创新的算法来构建参考质量标签数据，而只需很少的域输入 因此，大大降低了贴标签的成本。这将使我们能够应用ML技术来生成 已有的大量未标记数据的高质量标签，这反过来又将促进 潜在的定量成像生物标志物的评估。我们将开发、推广和评估小说 代表降低标签成本的三种不同策略的算法。这三种策略是 从结合了表征不确定性、优化样本的新策略的未标记数据中学习 用一种特别适合深度学习的新形式的主动学习选择专家质量标签， 并通过用一群人替换或增加专家来降低实现质量标签的成本 不是专家。然后，我们将实施和分发这些新颖的算法，以促进我们的 实验。最后，我们将通过将这三种策略应用于 确定最能捕捉脑内变化的定量成像生物标记物的重要挑战 与ASD的特征相关联的结构。信息学的这些基本进展 算法将降低成本并提高质量标签的获取率，这反过来将促进 广泛采用和部署机器学习算法进行图像解释。归根结底，这 将刺激新的成像生物标志物的开发，这些生物标志物具有显著改善的潜力 临床决策和患者结果。

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.