Towards Generalizable Reasoned Deep Learning for Efficient Interpretable Medical Image Computing

图像计算是一个关键的技术领域，在从工程到医学的许多领域都有应用。人工智能（AI）的一个子集，涉及创建能够模拟人类视觉的计算机系统或机器，该领域仍然面临着成像数据的巨大变化和不同的应用程序相关分析需求所带来的技术挑战。连接主义机器学习的浪潮最近席卷了该领域，其中系统动态地学习“通过对来自巨大数据库的标记图像示例进行训练来执行任务，而不是显式编程。特别是，深度学习（DL），其中使用松散地模拟人脑的多层人工神经网络，在一些应用中报告了前所未有的性能水平。医学图像计算对自动化分析提出了独特的技术挑战，限制了这一领域的成功。与纯认知视觉应用（如自动驾驶）截然不同，医学图像数据的分析和解释涉及与复杂知识库相关的高度专业化过程。因此，尽管它们具有明显的功能和普遍适用性，但当前的数据驱动的DL模型通常会导致肤浅的学习，并有令人担忧的模型脆弱性和意外失败的报告，对噪声的过度拟合和对有意义的数据的拟合不足，对新数据的泛化能力差和性能低，以及所需的训练数据库的不切实际性和高昂的标签成本。 这笔拨款将推动深度学习方法向深度推理方向发展，为医学图像计算等关键数据分析提供可靠和安全的人工智能。具体而言，所提出的技术方法将使数据驱动的DL模型超越视觉感知（看到），朝向集成逻辑和基于上下文的推理（理解、外推）发展，以便实现观察物理现实并结合领域知识的常识解决方案。我的工作将导致更普遍，可解释，有效和合理的学习模式，可以自我测试和自我提高，确保真实的生活效用，并显着提高成功采用的前景在战略重要性的加拿大人的领域。 通过所提出的技术方法实现的异质复杂医学图像数据分析的自动化将具有显著的社会经济影响，包括克服较差的医生与患者比率，降低医疗保健成本和延迟，提高诊断和护理提供的准确性和效率，以及实现健康研究的新发现和机会。我的研究将在人工智能领域培养一批多元化的未来专家和领导者，这是一个急需人才严重短缺的领域。它还将有助于确保加拿大仍然是人工智能研究的世界领导者，也是DL革命的主要贡献者。

Image computing is a key technology area with applications in numerous fields from engineering to medicine. A subset of artificial intelligence (AI) concerned with creating computer systems or machines capable of simulating human vision, the field still grapples with technical challenges arising from the vast variability in imaging data and disparate application-dependent analysis needs. A wave of connectionist machine learning has recently swept the field, in which systems dynamically learn' to perform a task through training on labelled image examples from huge databases rather than being explicitly programmed. In particular, deep learning (DL), in which multi-layer artificial neural nets that loosely model the human brain are used, has dominated with unprecedented performance levels reported in some applications. Medical image computing poses unique technical challenges to automated analysis that limited success in this area. Quite distinct from pure cognitive vision applications, such as autonomous driving, analysis and interpretation of medical image data involves highly specialized processes connected to complex knowledge bases. Hence, despite their apparent power and universal applicability, current data-driven DL models often result in superficial learning with alarming reports of model fragility and unexpected failures, overfitting to noise and underfitting to meaningful data, poor generalizability and low performance on new data, as well as impracticality of required training databases and prohibitive cost of labelling. This grant will advance DL methods towards deep reasoning' to enable reliable and safe AI for critical data analysis such as medical image computing. Specifically, the proposed technical methods will evolve data-driven DL models beyond visual perception (seeing) towards integrating logic and context based reasoning (understanding, extrapolating) in order to achieve common sense' solutions that observe physical realities and incorporate domain knowledge. My work will result in more generalizable, interpretable, efficient and reasoned learning models that can self-test and self-improve, ensuring real life utility and significantly improving the prospects of successful adoption in areas of strategic importance to Canadians. Automation of heterogenous complex medical image data analysis enabled by the proposed technical methods will have significant socio-economic impact including overcoming poor physician to patient ratio, reducing healthcare costs and delays, increasing accuracy and efficiency in diagnostics and care delivery, and enabling new findings and opportunities in health research. My research will train a diverse group of future experts and leaders in AI, a field where severe shortages in much needed talent exist. It will also help ensure Canada remains a world leader in AI research and a major contributor to the DL revolution'.