We are pleased to announce that our #ZeroCostDL4Mic work is now published!
nature.com/articles/s4146…
And our @github
github.com/HenriquesLab/Z…
#DeepLearning and #Microscopy for all!
For the occasion, I’ve put a small Tweetorial together to explain what this is all about!
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You can implement Deep Learning for microscopy in several ways:
1- Using local resources (fast GPUs)
2- Using cloud-computing
3- Using pre-trained models
We think that (1) is uncommon, and that (3) can be unreliable. So we do (2)!
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We built a platform using @GoogleColab and @ProjectJupyter notebooks that can perform training, quality control and prediction, all on the cloud and for free!
All of this dedicated to #microscopy !
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We implemented a large range of neural networks from some of the best Deep Learning developer teams in the world for segmentation, object detection, denoising, restauration, super-resolution, image-to-image translation, etc.
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We provide important features such as Data augmentation and Transfer learning. These allow efficient training of all the networks we provide.
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We put a strong emphasis on providing an easy way to assess model quality and performance. This is important to validate any model.
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We could not do it on our own and we got help from so many great researchers! Part 1/2
@ChamierLucas
@LaineBioImaging
@Liehuletti
@miCHRIScopy
@DanielKrentzel
@martina_lerche
@sara_mattilalab
@mattilalab
@Kar__El
@seamus_holden
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We could not do it on our own and we got help from so many great researchers! Part 2/2
@_ahmetcansolak
@sagzehn
@tibuch_
@martinjones78
@loicaroyer
@christlet
@ShechtmanLab
@florianjug
Mike Heilemann
@guijacquemet
@HenriquesLab
Some highlights! #DeepSTORM from @ShechtmanLab with data from @christlet
#Noise2Void from @sagzehn with some nice live data from iSIM!
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We also quantified the limitations of the approach (using @GoogleColab ) and present where it failed in our hands! Breaking points still allow us to train all our networks efficiently though.
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We also showed that it's possible to use our notebooks on other cloud-based platforms such as @DeepnoteHQ and @FloydHub_ , others are available too !
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