Towards Generalizable Reasoned Deep Learning for Efficient Interpretable Medical Image Computing

图像计算是一个关键技术领域，在从工程到医学的众多领域都有应用。作为人工智能(AI)的一个子集，该领域致力于创造能够模拟人类视觉的计算机系统或机器，但该领域仍在努力应对成像数据的巨大变异性和不同的依赖于应用的分析需求带来的技术挑战。最近，连接主义机器学习的浪潮席卷了这一领域，系统通过对来自大型数据库的标记图像样本进行训练来动态学习执行任务，而不是通过显式编程。尤其是深度学习，其中使用了多层人工神经网络来松散地模拟人脑，在一些应用中已经以前所未有的性能水平占据了主导地位。医学图像计算对自动分析提出了独特的技术挑战，限制了这一领域的成功。与自动驾驶等纯认知视觉应用程序截然不同的是，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的过程。因此，尽管目前的数据驱动的学习模型具有明显的能力和普遍的适用性，但它们往往导致肤浅的学习，报告显示模型的脆弱性和意外故障、对噪声的过度拟合和对有意义的数据的不足、较差的概括性和对新数据的低性能，以及所需的训练数据库的不切实际和高昂的标签成本。 这笔拨款将推动数字逻辑方法走向深度推理，以实现可靠和安全的人工智能，用于关键数据分析，如医学图像计算。具体地说，拟议的技术方法将使数据驱动的数字逻辑模型超越视觉感知(看到)，朝着整合逻辑和基于上下文的推理(理解、外推)的方向发展，以实现观察物理现实并结合领域知识的常识性解决方案。我的工作将产生更具普遍性、可解释性、高效和合理的学习模型，这些模型可以自我测试和自我改进，确保现实生活中的效用，并显著提高在对加拿大人具有战略重要性的领域成功采用的前景。 通过拟议的技术方法实现异质复杂医学图像数据分析的自动化将产生重大的社会经济影响，包括克服医患比差、降低医疗成本和延误、提高诊断和护理交付的准确性和效率，以及在卫生研究中实现新的发现和机会。我的研究将在人工智能领域培养一批不同的未来专家和领导者，这是一个亟需人才严重短缺的领域。它还将有助于确保加拿大在人工智能研究方面保持世界领先地位，并成为数字图书馆革命的主要贡献者。

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'.