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OCT 22, 2020 11:00 AM EDT

Impact of Segmentation Quality on Assay Endpoints - Deep Learning for High Content Screening

Speaker
  • Staff Analytics Engineer, Cytiva
    Biography

      Dr. Fuhui Long is a Staff Analytics Engineer at Cytiva, a global provider of technologies and services that advance and accelerate the development and manufacture of therapeutics. Her work over the years mainly focuses on developing machine learning, deep learning, and image analysis algorithms and software to help address challenging problems in biomedical applications and life sciences. Currently at Cytiva, she is leading the development of deep learning methods to segment nuclei, cells, and other organelles for the high content analysis software INCarta. Before joining Cytiva, she was with GE Healthcare briefly, working on the same topic. She worked as a senior machine learning R&D engineer at Allen Institute for Brain Science and a research scientist at Howard Hughes Medical Institute, where she developed very useful computational techniques for microscopy image analysis to help address questions in brain research and published high impact papers in Nature, Science, and Cell.


    Abstract

    As High Content Analysis (HCA) has matured and gained wider adoption as a quantitative research tool, the application space has grown and is no longer limited to a finite list of well defined assays performed in standard biological models. To account for this added complexity, a large focus has been placed on improving the flexibility and performance of analysis methods. Machine Learning is becoming ubiquitous and there are many examples these methods outperforming traditional methods for applications across many industries, scientific disciplines, and every day life. One would assume that technologies such as Deep Learning could provide similar benefits in HCA. But with any new technology, one has to weigh the potential benefits against the associated costs of adoption. We will present background information describing some of the fundamental differences between image analysis methods, considerations one should keep in mind when evaluating analysis methods, and some examples suggesting the improvements one could realize by employing technologies such as Deep Learning.

    Learning Objectives

    • Limitations of image processing methods and the advantages of machine learning for object segmentation
    • Survey of machine learning approaches and key differences with respect to their application in HCA
    • The impact of improved segmentation accuracy on the overall endpoint of High Content screens

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