AI microscopy symposium and workshop
Hosted by Aivia and Leica Microsystems
Fred Hutch Cancer Research Center | POSTPONED
Important event advisory (10/12/2020)

In light of the continuing Covid-19 pandemic, we have decided to make this event virtual for everyone around the world. Please refer to this page for the most up-to-date information about the event.

This event is focused on artificial intelligence-guided techniques for microscopy. Join us on Monday, March 9th for a symposium on AI applications in microscopy. In the afternoon, you can get hands-on experience with open-source and commercial AI tools like the Allen Cell Segmenter and Aivia.

Supported by
Hosted by
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Guest speakers

The symposium focuses on recent developments in machine learning and deep learning tools for microscopy. Researchers from all backgrounds who are interested in AI microscopy are encouraged to attend. The speakers for this symposium are as follows:

 
Phuc "Sam" Nguyen
Graduate Student, Kueh lab
University of Washington
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Susanne Rafelski
Director of Assay Development
Allen Institute of Cell Science
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Luciano Lucas
Executive Vice President
Aivia
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Theo Knijnenburg
Director of Modeling
Allen Institute of Cell Science
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Wan-Qing Yu
Postdoctoral Fellow, Wong lab
University of Washington
Julien Dubrulle
Staff Scientist
Fred Hutch

Schedule and agenda

Date to be announced
Fred Hutch Cancer Research Center, Location TBD

9:20 - 9:30 am

9:30 - 10:15 am

10:15 - 10:30 am

10:30 - 11:15 am

 

11:15 - 12:00 noon

 

12:00 - 1:00 pm

1:00 - 1:45 pm

 

1:45 - 2:00 pm

2:00 - 2:45 pm

2:45 - 3:30 pm

3:30 - 3:45 pm

3:45 - 5:00 pm

Introductions

Sam Nguyen, "Deep learning based morphological profiling for cell state transition analysis in unmanipulated cells."

Q&A and coffee break

Susanne Rafelski, "An iterative deep learning approach for image-based quantification of stem cell structures."

Luciano Lucas, "Deep learning for image restoration: how to minimize the intrinsic limitation of light microscopy."

Lunch break

Theo Knijnenburg, "Machine learning based integrated cell models and label free imaging."

Q&A and coffee break

Wan-Qing Yu, "AI-aided neural circuit reconstruction in mouse retina."

Julien Dubrulle, "Image analysis services at Fred Hutch."

Q&A and coffee

Demonstrations of image analysis tools available from Aivia, Allen Institute, and Fred Hutch Shared Resources Imaging Facility

 

Registration

For more information and to register for the event, please complete the form below. ​

Monday, March 9, 2020
Fred Hutch Cancer Research Center, Weintraub Building, Pelton Auditorium Room B1-065

9:20 - 9:30 am

Introductions

9:30 - 10:15 am

Sam Nguyen, "Deep learning based morphological profiling of cell state transition analysis in unmanipulated cells."

10:15 -10:30 am

Q&A and coffee break

10:30 - 11:15 am

Susanne Rafelski, "An iterative deep learning approach for image-based quantification of stem cell structures."

11:15 - 12:00 noon

Luciano Lucas, "Deep learning for image restoration: how to minimize the intrinsic limitation of light microscopy."

12:00 - 1:00 pm

Lunch break

1:00 - 1:45 pm

Theo Knijnenburg, "Machine learning based integrated cell modes and label free imaging."

 

1:45 - 2:00 pm

Q&A and coffee break

2:00 - 2:45 pm

Wan-Qing Yu, "AI-aided neural circuit reconstruction in mouse retina."

2:45 - 3:30 pm

Julien Dubrulle, "Image analysis services at Fred Hutch."

3:30 - 3:45 pm

Q&A and coffee break

3:45 - 5:00 pm

Demonstrations of image analysis tools available from Aivia, Allen Institute, and Fred Hutch Shared Resources Imaging Facility