Deep learning for medical computer vision: Beyond more data and more computing power

医学计算机视觉深度学习：超越更多数据和更强计算能力

• 批准号: RGPIN-2020-06752
• 负责人: Hamarneh, Ghassan
• 金额: $ 4.01万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 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=750561
• 关键词: Deep; learning; medical; computer; vision

Image data is collected for a variety of applications, e.g., manufacturing, transportation, astronomy, security, and agriculture, as interpreting these images can lead to new insights and discoveries, better-informed decisions, and increased productivity. Biomedical images have revolutionized biology and medicine by giving clinicians and scientists visual access to ex/in-vivo anatomy and function of cells, tissues, organs, and whole organisms in healthy and diseased states. As the number and size of biomedical images are growing rapidly, finer details are captured in shorter times, and image dimensionality is increasing from scalar 2D to dynamic multi-valued 3D, images can no longer be interpreted via manual visual inspection. Medical computer vision (MCV), the topic of this proposed research, is the discipline tasked with developing computer systems that interpret biomedical images. In recent years, deep learning (DL), a subset of machine learning, which in turn is a subset of artificial intelligence, has become the de-facto computational methodology for tackling MCV problems across the spectrums of imaging modalities and clinical applications. The impressive superior performance results of DL methods, reported in research papers, are difficult to ignore. DL is attracting extraordinary attention and the availability of relatively easy to use DL tools is bringing an onrush of users transfixed by the perceived promise that DL is the solution to all problems. Undoubtedly DL has something valuable to offer towards addressing MCV problems. However, there are important challenges to overcome, beyond naïvely seeking more data and more computing power, before DL-based MCV technologies become trusted, reliable components that can be deployed in critical bioimaging-driven clinical workflows or biological discoveries that ultimately can lead to advancing science and improving healthcare. The proposed research focuses on creating novel automated MCV techniques capable of accurate, robust, and fast bioimage interpretation by tackling the critical issues surrounding DL and DL-based MCV, such as, fairness, generalizability, explainability, data-reliance, trust, and model design. The proposed research aims at answering the following questions: How to identify, enhance, and leverage resources to train DL MCV systems (e.g., raw image data, example interpretations, and domain-knowledge)? What is the landscape of possible systems and how to explore it in order to arrive at useful systems? What are the different criteria and tradeoffs involved in assessing such systems (e.g., accuracy and explainability)? And what are some of the computational challenges that arise when considering real-world deployment of such systems (e.g., data privacy and continual learning)?

为各种应用程序收集了图像数据，例如制造，运输，天文学，安全和同意，以解释这些图像可以导致新的见解和发现，更有信息的决策以及提高生产率。生物医学图像通过使临床医生和科学家的视觉访问能够访问前/体内解剖结构以及细胞，组织，器官和整个生物体在健康和脱离状态的状态，从而改变了生物学和医学。随着生物医学图像的数量和大小正在迅速增长，最终细节会在较短的时间捕获，并且图像维度从标量2D到动态多值3D的增加，不再可以通过手动视觉检查来解释图像。这项提出的研究的主题是医疗计算机视觉（MCV），是该学科的纪律，其旨在开发解释生物医学图像的计算机系统。近年来，《深度学习》（DL）的一部分机器学习又是人工智能的子集，已成为解决成像模式和临床应用范围内MCV问题的事实计算方法。在研究论文中报道的DL方法的令人印象深刻的出色性能结果很难忽略。 DL引起了非凡的关注，相对易于使用的DL工具的可用性正在引起人们对用户的泛滥，因为人们认为DL是解决所有问题的解决方案。毫无疑问，DL可以为解决MCV问题提供一些有价值的东西。但是，在基于DL的MCV技术成为可信赖的，可靠的组件之前，还需要克服重要的挑战，除了幼稚寻求更多的数据和更多的计算能力之外，可以将其部署在关键的生物成像驱动的临床工作流或生物学发现中，这些发现最终可以导致进步科学和改善医疗保健。拟议的研究着重于创建能够通过解决基于DL和DL的MCV的关键问题（例如公平，公平性，普遍性，解释，数据依据，信任和模型设计）来创建能够准确，健壮和快速生物形象解释的新型自动MCV技术。拟议的研究旨在回答以下问题：如何识别，增强和利用培训DL MCV系统（例如原始图像数据，示例解释和域知识）的资源？为了获得有用的系统，可能的系统的景观是什么？如何探索它？评估此类系统（例如准确性和解释性）涉及哪些不同的标准和权衡？在考虑此类系统的现实部署（例如数据隐私和持续学习）时，会出现哪些计算挑战？