Toward a Thinking Microscope: Deep Learning-enabled Computational Microscopy and Sensing
迈向智慧显微镜：基于深度学习的计算显微成像和传感技术
Aydogan Ozcan
ABSRACT
Deep learning is a class of machine learning techniques that uses multi-layered artificial neural networks for automated analysis of signals or data. The name comes from the general structure of deep neural networks, which consist of several layers of artificial neurons, each performing a nonlinear operation, stacked over each other. Beyond its main stream applications such as the recognition and labeling of specific features in images, deep learning holds numerous opportunities for revolutionizing image formation, reconstruction and sensing fields. In fact, deep learning is mysteriously powerful and has been surprising optics researchers in what it can achieve for advancing optical microscopy, and introducing new image reconstruction and transformation methods. From physics-inspired optical designs and devices, we are moving toward data-driven designs that will holistically change both optical hardware and software of next generation microscopy and sensing, blending the two in new ways. Today, we sample an image and then act on it using a computer. Powered by deep learning, next generation optical microscopes and sensors will understand a scene or an object and accordingly decide on how and what to sample based on a given task – this will require a perfect marriage of deep learning with new optical microscopy hardware that is designed based on data. For such a thinking microscope, unsupervised learning would be the key to scale up its impact on various areas of science and engineering, where access to labeled image data might not be immediately available or very costly, difficult to acquire. In this presentation, I will provide an overview of some of our recent work on the use of deep neural networks in advancing computational microscopy and sensing systems, also covering their biomedical applications.
深度学习是机器学习技术的一类，它使用多层人工神经网络来自动分析信号或数据。该名称来自深度神经网络的一般结构，即该结构由几层人工神经元组成，每层人工神经元彼此堆叠，每层神经元执行一次非线性运算。除了其主流应用 (例如图像中特定特征的识别和标记) 之外，深度学习还拥有许多革新性的图像形生成和重建，以及传感领域的应用。实际上，深度学习具有不可思议的强大功能。它发展了光学显微技术，引入新的图像重建和变换方法，带来了令光学研究人员惊讶的成果。我们正从物理驱动的光学设计和设备，朝着数据驱动的设计的方向发展。这些设计将全面改变下一代显微镜和传感的光学硬件和软件，并以新的方式将两者融合。当前，我们采集图像，然后使用计算机进行处理。在深度学习的支持下，下一代光学显微镜和传感器将感知场景或对象，并根据给定的任务决定采样的方式和内容–这将需要深度学习与新设计的光学显微镜硬件的基于数据的完美结合。对于这种智慧显微镜而言，无监督学习将是扩大其对科学和工程学各个领域影响的关键。因为在这些领域中，标注好的图像数据可能难获得或者成本高昂。在本演讲中，我将概述我们在使用深层神经网络推进计算显微成像和传感系统方面的最新工作，其中还涵盖了它们在生物医学领域的应用。
BIOGRAPHY
Dr. Ozcan is the Chancellor’s Professor and the Volgenau Chair for Engineering Innovation at UCLA and an HHMI Professor with the Howard Hughes Medical Institute, leading the Bio- and Nano-Photonics Laboratory at UCLA and is also the Associate Director of the California NanoSystems Institute. Dr. Ozcan is elected Fellow of the National Academy of Inventors (NAI) and holds 41 issued patents and >20 pending patent applications and is also the author of one book and the co-author of >700 peer-reviewed publications in major scientific journals and conferences. Dr. Ozcan is the founder and a member of the Board of Directors of Lucendi Inc., Pictor Labs and Holomic/Cellmic LLC, which was named a Technology Pioneer by The World Economic Forum in 2015. Dr. Ozcan is also a Fellow of the American Association for the Advancement of Science (AAAS) , the International Photonics Society (SPIE) , the Optical Society of America (OSA) , the American Institute for Medical and Biological Engineering (AIMBE) , the Institute of Electrical and Electronics Engineers (IEEE) , the Royal Society of Chemistry (RSC) , the American Physical Society (APS) and the Guggenheim Foundation, and has received major awards including the Presidential Early Career Award for Scientists and Engineers, International Commission for Optics Prize, Biophotonics Technology Innovator Award, Rahmi M. Koc Science Medal, International Photonics Society Early Career Achievement Award, Army Young Investigator Award, NSF CAREER Award, NIH Director’s New Innovator Award, Navy Young Investigator Award, IEEE Photonics Society Young Investigator Award and Distinguished Lecturer Award, National Geographic Emerging Explorer Award, National Academy of Engineering The Grainger Foundation Frontiers of Engineering Award and MIT’s TR35 Award for his seminal contributions to computational imaging, sensing and diagnostics.
Ozcan博士是UCLA的校长教授和Volgenau工程创新主席，以及霍华德休斯医学院的HHMI教授，领导UCLA的生物和纳米光子学实验室，同时还是加利福尼亚州纳米系统研究所的副主任。 Ozcan博士当选为美国国家发明院 (NAI) 院士，拥有41项已发布的专利和20多项正在申请的专利申请，还著有一本书的和主要科学期刊及会议上700余篇经同行评审的文章。 Ozcan博士是Lucendi Inc.，Pictor Labs和Holomic / Cellmic LLC的创始人和董事会成员，后者于2015年被世界经济论坛评选为技术先驱。Ozcan博士还是美国科学促进会 (AAAS)，国际光子学会 (SPIE)，美国光学学会 (OSA)，美国医学和生物工程学院 (AIMBE)，电气电子工程师学会 (IEEE)，英国皇家化学学会 (RSC)，美国物理学会 (APS) 和古根海姆基金会的成员，并获得了重要奖项，包括科学家和工程师早期职业总统奖，国际光学委员会奖，生物光子技术创新奖，Rahmi M. Koc科学奖章，国际光子学会早期职业成就奖，陆军年轻研究者奖，美国国家科学基金会职业奖，国立卫生研究院主任新创新者奖，海军年轻研究者奖，IEEE光子学会青年研究奖和杰出讲师奖，国家地理新兴探索者奖，国家工程院Grainger基金会前沿工程奖和MIT TR35奖，表彰他对计算成像，传感和诊断的开创性贡献。