BIIF Newsletter, February 2021

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Dear All,

In this information e-mail we inform you about upcoming seminars, the new support sessions and an exciting Kaggle competition. We have an upcoming event on March 11 - in this event we will present typical image analysis projects and which support we offer – followed by discussion rounds in smaller groups. Please spread the word about this event to your users!
Also, we are thrilled that Gisele Miranda from BIIF became Imaging Scientist of the prestigious Chan-Zuckerberg Initiative! Congratulations!
Further, BIIF was mentioned in this nature technology feature about machine learning for “cleaning up” microscopy images. If you get inspired and want to use traditional methods and/or deep learning to de-noise your images, please get in contact with us.

Support

You can always contact us via biif@scilifelab.se for support on image analysis. Additionally, we offer Call4Help sessions. Here we team up with Sylvie LeGuyader, LCI, Karolinska Institutet, to give combined advice on both microscopy and image analysis. The next Call4Help session is on March 2, 2021 – find more information online. Also, do check out our project database to get inspiration and examples of image analysis projects.

Upcoming Seminars

BioImage Informatics Facility: Examples of using image analysis in life science projects.

Online Event In this event we will present several user projects to show you how we have used image analysis in different areas of life science research. After the presentation there will be time for more interactive discussions in topics-/tool specific breakout rooms.

NEUBIAS Academy

There are two upcoming webinars (tba) on KNIME with NEUBIAS Academy. Save the dates: March 4 and 11, 2021 , 15h30 - 17h00. KNIME Analytics Platform is an easy to use open platform designed to handle data. The two sessions will focus on how to handle images with KNIME.

Kaggle Competition

The Human Protein Atlas Team, lead by Professor Emma Lundberg, launched a new kaggle competition. In this Human Protein Atlas Single Cell Classification Challenge the task is to classify protein patterns in single cells based on labels given only on image-level (weak supervision).

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