Towards Generalizable Reasoned Deep Learning for Efficient Interpretable Medical Image Computing

迈向可泛化推理深度学习以实现高效可解释医学图像计算

• 批准号: RGPIN-2020-06179
• 负责人: Garbi, Rafeef
• 金额: $ 3.35万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=741078
• 关键词: Towards; Generalizable; Reasoned; Deep; Learning

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'.

图像计算是一个关键技术领域，在工程到医学的许多领域中都有应用。人工智能（AI）的一部分涉及创建能够模拟人类视力的计算机系统或机器，该领域仍在努力应对成像数据中的巨大可变性和不同应用程序依赖性分析需求引起的技术挑战。连接式机器学习浪潮最近席卷了该领域，在该领域中，系统动态地“学习”通过对巨大数据库的标记图像示例进行培训，而不是被明确编程。尤其是，深度学习（DL），其中使用了对人脑的宽松建模的多层人造神经网已经以某些应用中报道的前所未有的性能水平主导。医学图像计算对自动化分析构成了独特的技术挑战，从而有限于该领域的成功。与纯粹的认知视觉应用完全不同，例如对医学图像数据的自主驾驶，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，尽管具有明显的功率和通用的适用性，但当前的数据驱动的DL模型通常会导致肤浅的学习，并具有令人震惊的模型脆弱性和意外故障的报道，过度适合噪音，并且不适合有意义的数据，差的可推广性和较低的新数据性能以及所需培训数据库的不合理性以及标记的训练数据库成本。该赠款将推动DL方法朝着“深度推理”促进可靠且安全的AI，以进行关键数据分析，例如医学图像计算。具体而言，所提出的技术方法将在视觉感知之外（看到）发展以数据驱动的DL模型为基于逻辑和上下文的推理（理解，外推），以实现观察物理现实并结合域知识的“常识”解决方案。我的工作将导致更具概括，解释，高效和理性的学习模型，这些学习模型可以自我测试和自我消灭，确保现实生活实用程序，并显着改善了在对加拿大人具有战略重要性领域成功采用的前景。 由提议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产生重大的社会经济影响，包括克服较差的医师与患者比率，降低医疗保健成本和延误，提高诊断和护理的准确性和效率，并在健康研究中实现新的发现和机会。我的研究将培训AI的一群未来的专家和领导者组，该领域存在急需人才的严重短缺。这还将有助于确保加拿大仍然是AI研究的世界领导者，并为“ DL革命”的主要贡献者。