The Moscone Center
San Francisco, California, United States
23 - 28 January 2021
Conference BO508
High-Speed Biomedical Imaging and Spectroscopy VI
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Abstract Due:
15 July 2020

Author Notification:
21 September 2020

Manuscript Due Date:
5 January 2021


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Conference Chairs
Program Committee
Program Committee continued...
Additional Conference
Call for

Submissions to this conference include the following:
  • 100-word text abstract (for online program)
  • 250-word text abstract (for abstract digest)
  • 2-page extended abstract (for committee review only). The extended abstract must be submitted as a separate PDF document limited to two pages, including tables and figures. Include author names and affiliations; text; any figures; tables, or images; and sufficient data to permit committee review.
  • Supplementary files (optional). Any supplementary file that may help reviewers can be submitted with the abstracts.

All submissions will be reviewed by the Program Committee to determine acceptance. Extended abstracts will be used only for the purpose of review, and will not be published.

Real-time capture and analysis of fast, non-repetitive, dynamical events has long been a challenging problem in the field of instrumentation for biomedical research. Notable examples include momentous efforts on establishing high-speed optical spectroscopy with high spectral resolution and wide spectral range (e.g., Raman spectroscopy, Fourier-transform infrared spectroscopy, THz spectroscopy, and fluorescence detection) and high-speed optical microscopy with high spatial resolution and wide field of view (e.g., light-sheet microscopy, ultrafast imaging, 4D imaging, photoacoustic microscopy, super-resolution fluorescence microscopy, and image cytometry). The development of such high-speed optical instruments is driven by the need for better understanding dynamical processes in biological systems such as neural activity and calcium transport as well as for high-throughput quantitative analysis of heterogeneous cell populations such as blood cells and stem cells. The big data produced by the high-speed optical instruments is well aligned with the pressing need for progressively larger biomedical datasets for efficient and accurate data analysis with the help of machine learning (e.g., deep learning) to make better decisions in biomedical research and clinical diagnosis.

The aim of this Conference is to bring researchers specialized in high-speed optical bioinstrumentation, data management, and high-speed signal/image processing together in a single multidisciplinary forum. With the presentations of the latest developments, this Conference is intended to serve as an arena to promote idea exchanges, interdisciplinary collaborations, and technological advancements in this new and exciting field of high-speed optical bioinstrumentation with focuses on its future trend and development.
::This conference intends to cover, but not limited to, the following topics:

Methods for high-speed optical imaging and spectroscopy
  • high-speed spectroscopy and fluorescence detection
  • high-throughput imaging
  • ultrafast imaging techniques
  • 4D imaging (high-speed volumetric imaging)
  • high-throughput lensless microscopy
  • high-throughput light-sheet microscopy
  • high-speed photoacoustic imaging
  • high-speed super-resolution imaging
  • high-speed multiphoton fluorescence imaging
  • high-speed coherent Raman spectroscopy/imaging (e.g., SRS, CARS)
  • biomedical applications of high-speed optical spectroscopy and imaging
  • neural imaging
  • flow cytometry
  • image cytometry
  • high-throughput screening
  • high-content screening
  • high-throughput histopathology
  • real-time functional in vivo imaging.
Computational methods for signal/image processing and big data management
  • instrumentation for real-time computational imaging/spectroscopy (e.g., FPGA, GPU)
  • computationally efficient algorithms
  • data compression and compressive sensing
  • high-speed digital signal/image processing
  • high-dimensional image analysis
  • data mining.
Machine learning for high-speed optical spectroscopy and imaging
  • deep learning
  • artificial neural networks
  • efficient classification and inference algorithms.
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