Medical Image Analysis

医学图像分析

• 批准号: CRC-2021-00069
• 负责人: Forkert, NilsDaniel
• 金额: $ 7.29万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Canada Research Chairs
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=764446
• 关键词: Medical; Image; Analysis

In a data driven world, machine learning is expected to be a key tool for converting big data into tangible benefits. Neuroscience studies in particular collect large and complex data on a single patient, including brain imaging, functional scores, and clinical information, to adequately capture the complexities of brain diseases. Lately, advances in machine learning, and especially deep learning, have enabled the development of sophisticated computer-aided diagnosis tools for challenging tasks in many medical domains (e.g. dermatology or ophthalmology). Without doubt, novel machine learning methods also have huge potential for developing advanced methods for computer-aided data analysis (e.g. image segmentation), classification, and prognosis in neurological diseases. However, many datasets are typically needed to train robust and accurate machine learning models. This is a major reason preventing the development of sophisticated machine learning models for health care applications in general and in the neuroscience domain in particular because concerns about data privacy and sharing practices are becoming increasingly important while patients also have a better understanding of the value of their personal data and are increasingly concerned about data privacy. For this reason, we face the risk that novel data mining and machine learning approaches will not achieve their true potential for diagnosis and treatment support in patients with neurological diseases.Therefore, the aim of this CRC research program is to develop a novel distributed learning system for training machine learning models that does not require data sharing and centralized collection. Here, the patient data used for machine learning model training does not leave the contributing institution and is only used locally to train machine learning models on-site. The machine learning model, which does not contain any personally identifiable information, is then shared between institutions or with a central server where it is combined to a global model. Using this approach, many advanced machine learning models can be trained for computer-aided diagnosis of various diseases, which will have significant impact on neuroscience research and beyond.

在数据驱动的世界里，机器学习有望成为将大数据转化为有形利益的关键工具。神经科学研究尤其收集单个患者的大量复杂数据，包括脑成像、功能评分和临床信息，以充分捕捉脑部疾病的复杂性。最近，机器学习的进步，特别是深度学习的进步，使得复杂的计算机辅助诊断工具能够用于许多医学领域（例如皮肤病学或眼科）的挑战性任务。毫无疑问，新的机器学习方法在开发计算机辅助数据分析（例如图像分割）、分类和神经系统疾病预后的先进方法方面也具有巨大的潜力。然而，通常需要许多数据集来训练鲁棒和准确的机器学习模型。这是阻碍为医疗保健应用开发复杂机器学习模型的主要原因，特别是在神经科学领域，因为对数据隐私和共享实践的担忧变得越来越重要，同时患者也对其个人数据的价值有了更好的了解，并且越来越关注数据隐私。由于这个原因，我们面临的风险是，新的数据挖掘和机器学习方法将无法发挥其在神经系统疾病患者诊断和治疗支持方面的真正潜力。因此，本CRC研究计划的目的是开发一种新的分布式学习系统，用于训练不需要数据共享和集中收集的机器学习模型。在这里，用于机器学习模型训练的患者数据不会离开贡献机构，仅在本地用于现场训练机器学习模型。机器学习模型不包含任何个人身份信息，然后在机构之间或与中央服务器共享，并将其合并为全球模型。利用这种方法，可以训练许多先进的机器学习模型，用于各种疾病的计算机辅助诊断，这将对神经科学等领域的研究产生重大影响。