Machine learning approaches to image analysis

我的研究计划专注于开发新的计算方法和分析工具，用于医疗保健政策和交付，与机器学习(ML)与计算能力进步的交集提供的机会保持一致。这包括开发和优化基于代理的建模框架，以模拟大规模感染传播，以及在mHealth环境中的图像处理方面的进展。总体研究的重点是为政策和临床实践提供更好的基于技术的决策工具。图像识别和图像分析是一种发展非常迅速的人工智能形式，用于各种社交和商业环境，从社交媒体上的自动组织图像到无人驾驶车辆识别道路上的障碍物。医学图像处理是该领域内的一个专业，近年来因其在临床决策支持、诊断工具、精确医学和人口健康方面的预期改变游戏规则的应用而受到关注。医学图像处理是一个复杂的问题。人们普遍认为，与其他图像识别应用领域相比，风险通常更高，所需的可靠性水平滞后。有意义的医学图像分析需要海量的数据，而目前许多类别的医学图像都无法获得这些数据。更高分辨率和3维医学图像的趋势，再加上对效率的期望，带来了新的计算挑战。医学图像处理没有单一的最优算法，最好的方法还是个未知数。这项研究计划将利用ML与图像处理中的分析工具相结合的巨大进步，构建和优化医学图像分析的算法管道。缺乏有标签的伤口数据带来了挑战和复杂性。这项研究将在只有内置传感器和摄像头的现成移动设备的限制下展开。这带来了比MRI或CT成像更高的图像变异性，但反映了我们在移动设备上收集、研究和共享信息的患者驱动的医疗保健。目标是开发新的图像分析方法，为加拿大医疗保健提供决策、诊断和远程医疗工具。为此，这项工作将开发工具，将伤口图像的医学图像数据众包，解决目前研究所需的空白，并提供一个数据库供其他研究人员使用。这项工作将构建和优化算法，以准确有效地识别医学图像中的特征并对其进行分类。特别令人感兴趣的是生成性对抗性网络，这是一种在训练数据有限时很有前途的最大似然方法，以及卷积神经网络，它是几种在人脑上松散建模并很好地适应图像分类的最大似然方法之一。

My research program focusses on developing new computational approaches and analytical tools for healthcare policy and delivery, aligned with opportunities afforded by the intersection of machine learning (ML) with advances in computing power. This has included development and optimization of agent-based modeling frameworks to simulate large-scale infection spread and advances in image processing in mHealth contexts. The overall research focus is on better technology-based decision-making tools for policy and clinical practice. Image recognition and image analysis is a very rapidly growing form of artificial intelligence used in a myriad of social and business contexts, from auto-organizing images in social media to driverless vehicles recognizing obstacles in their path. Medical image processing is a specialization within the field, gaining attention in recent years for its anticipated game-changing applications in clinical decision support, diagnostic tools, precision medicine, and population health. Medical image processing is complex. It is widely recognized that the stakes are generally higher than in other image recognition application areas, with required levels of reliability lagging. Meaningful medical image analysis requires massive amounts of data, which are currently not available for many classes of medical images. The trend toward higher resolution and 3-dimensional medical images, combined with expectation of efficiency create new computational challenges. There is no single best algorithm applied to medical image processing, leaving the best methods as an open question. This research program will construct and optimize algorithmic pipelines for medical image analysis, leveraging the enormous progress in ML combined with analytical tools in image processing. The lack of labelled wound data creates challenge and complexity. The research will unfold within the constraints of off-the-shelf mobile devices with only built-in sensors and cameras. This introduces significantly higher variability in images than MRI or CT imaging, yet reflects patient-driven healthcare in which we collect, research, and share information on our mobile devices. The goal is to develop new approaches to image analysis that can offer decision, diagnostic, and telehealth tools to Canadian healthcare. To do so, the work will develop tools to crowdsource medical image data for wound images, addressing a current gap required for the research and providing a database for other researchers' use. The work will construct and optimize algorithms to accurately and efficiently identify features and classify them in medical images. Of specific interest are generative adversarial networks, a promising ML approach when training data are limited, and convolutional neural networks, one of several ML approaches modeled loosely on the human brain and well-adapted to image classification.