Methods for reliable machine learning with applications in medical imaging

该研究项目涉及开发机器学习（ML）和模式识别方面的先进计算技术，以及应用于大规模真实世界医学成像检查的新型验证和评估技术，以追求下一代医学诊断应用。所提出的技术和机器学习评估方法将朝着创建诊断和疾病表征技术的方向发展，这些技术可以帮助提高神经发育障碍儿童的患者护理标准。开发的技术将通过对患有各种疾病（如自闭症、注意缺陷多动障碍（ADHD）等）的患者进行MRI检查的大型数据集进行评估。这将包括在公开可用的MRI检查中评估拟议的新型ML技术，并将这些技术转化为基于常规临床影像学检查的应用（波士顿儿童医院，哈佛医学院，我在那里担任研究助理）。这将支持更大规模的评估，作为该提案的一部分开发的新提议技术是否在现实的临床人群中发挥潜在作用。广泛的常规临床影像学检查的可用性支持了对各种医学疾病（多发性硬化症、脑瘫、神经纤维瘤病、精神分裂症等）的创新技术的验证。******拥有多学科技术背景（计算，医学物理）和跨学科医学研究（神经科学，生物医学工程）的丰富经验以及许多新的研究途径，我在这项拟议的研究中具有独特的优势。该研究项目将涉及使用现有模式识别技术从大型数据集中提取感兴趣的测量值，同时开发新的通用机器学习算法和验证方法，并在大量脑MRI检查中进行广泛评估。机器学习将用于结合模式识别技术提取的测量值，以改进诊断和疾病表征。该建议将涉及使用现有技术，以及开发基于我的工作已有贡献的新型ML方法，包括允许随测试偏差设置而变化的灵活决策边界的公式，为诊断测试提供优化。******将采用严格的统计验证技术，并尽可能使用独立数据集（自闭症，ADHD等）确认结果。将开发新的验证技术来评估样本量和误差一致性问题。这些验证技术将是通用的，因此可以被所有ML应用程序开发人员使用。* * * * *

This research program involves the development of advanced computational technologies in machine learning (ML) and pattern recognition along with novel validation and evaluation techniques applied to large-scale real world medical imaging examinations in pursuit of the next generation of applications in medical diagnostics. The proposed technological and ML evaluation methods will be developed towards the creation of diagnostic and disease characterization technologies that can help improve the standard of patient care for children with neurodevelopmental disorders. Technologies developed will be evaluated with large datasets of MRI examinations of patients with a variety of conditions such as autism, attention deficit hyperactivity disorder (ADHD) and more. This will include the evaluation of proposed novel ML technology on publicly available MRI examinations and the translation of those technologies to applications based on routine clinical imaging exams (Boston Children's Hospital, Harvard Medical School, where I hold an appointment as a Research Associate). This will support larger scale evaluation of whether novel proposed technologies developed as part of this proposal have a potential role to play in a realistic clinical population. The availability of extensive routine clinical imaging examinations supports the validation of created technologies across an assortment of medical disorders (multiple sclerosis, cerebral palsy, neurofibromatosis, schizophrenia and more).******With a multidisciplinary technical background (computation, medical physics) and extensive experience in interdisciplinary medical research (neuroscience, biomedical engineering) and many novel research avenues identified, I am uniquely positioned to succeed in this proposed research. This research program will involve the extraction of measurements of interest from large datasets using existing pattern recognition techniques along with the development of novel general purpose ML algorithms and validation approaches extensively assessed on large collections of brain MRI examinations. ML will be employed to combine the measurements extracted by the pattern recognition techniques to improve diagnostics and disorder characterization. This proposal will involve the use of existing techniques, as well as the development of novel ML methods that build on the pre-existing contributions of my work, including formulations which allow for a flexible decision boundary that varies with the test bias setting, providing optimizations for diagnostic testing.******Rigorous statistical validation techniques will be employed and results will be confirmed with independent datasets wherever possible (autism, ADHD, etc.). Novel validation techniques will be developed to assess sample size and error consistency issues. These validation techniques will be general-purpose and thus can be used by all ML application developers. *****