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

为各种应用收集图像数据，例如，这些图像可以用于制造业、交通运输、天文学、安全和农业，因为解释这些图像可以带来新的见解和发现，更明智的决策和提高生产力。生物医学图像通过使临床医生和科学家能够直观地了解健康和患病状态下的细胞、组织、器官和整个生物体的离体/体内解剖结构和功能，彻底改变了生物学和医学。随着生物医学图像的数量和尺寸迅速增长，在更短的时间内捕获更精细的细节，图像维度从标量2D增加到动态多值3D，图像不再能够通过手动视觉检查来解释。医学计算机视觉（MCV）是这项拟议研究的主题，是一门负责开发解释生物医学图像的计算机系统的学科。 近年来，深度学习（DL）是机器学习的一个子集，而机器学习又是人工智能的一个子集，它已经成为解决成像模式和临床应用范围内MCV问题的事实上的计算方法。研究论文中报告的DL方法令人印象深刻的上级性能结果是难以忽视的。深度学习正吸引着人们的极大关注，相对易于使用的深度学习工具的可用性正带来大量用户，他们被深度学习是所有问题的解决方案的承诺所震惊。毫无疑问，DL可以为解决MCV问题提供一些有价值的东西。然而，在基于DL的MCV技术成为可信、可靠的组件之前，除了单纯地寻求更多的数据和更多的计算能力之外，还有一些重要的挑战需要克服，这些组件可以部署在关键的生物成像驱动的临床工作流程或生物发现中，最终可以推动科学发展和改善医疗保健。 拟议的研究重点是通过解决围绕DL和基于DL的MCV的关键问题，如公平性，可推广性，可解释性，数据依赖性，信任和模型设计，创建新的自动化MCV技术，能够准确，鲁棒和快速的生物图像解释。拟议的研究旨在回答以下问题：如何识别，增强和利用资源来训练DL MCV系统（例如，原始图像数据、示例解释和领域知识）？什么是可能的系统的景观，以及如何探索它，以达到有用的系统？在评估这种系统时涉及哪些不同的标准和权衡（例如，准确性和可解释性）。在考虑此类系统的实际部署时，会出现哪些计算挑战（例如，数据隐私和持续学习）？

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 navely 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)?