Discover and read the best of Twitter Threads about #zerocostdl4mic
Most recents (2)
1/21
We’re proud to present a new Deep Learning approach for Content-Aware Frame Interpolation (CAFI). Ever wanted to increase the frame rate of your images using Deep Learning? CAFI might be the right tool for you.
It’s CAFI time!
github.com/mpriessner/CAFI
biorxiv.org/content/10.110…
We’re proud to present a new Deep Learning approach for Content-Aware Frame Interpolation (CAFI). Ever wanted to increase the frame rate of your images using Deep Learning? CAFI might be the right tool for you.
It’s CAFI time!
github.com/mpriessner/CAFI
biorxiv.org/content/10.110…
2/21
Delighted to share this work, a collaboration with @dgaboriau, @SheridanArlo, Tchern Lenn & Jonathan Chubb (not on Twitter), @manorlaboratory, @Vilar_lab and @LaineBioImaging
biorxiv.org/content/10.110…
Delighted to share this work, a collaboration with @dgaboriau, @SheridanArlo, Tchern Lenn & Jonathan Chubb (not on Twitter), @manorlaboratory, @Vilar_lab and @LaineBioImaging
biorxiv.org/content/10.110…
3/21
CAFI provides Deep Learning-based temporal super-resolution for fast bioimaging. It increases the frame rate of any microscope modality by interpolating an image in between two consecutive images via “intelligent” interpolation, providing 2x increase in temporal resolution.
CAFI provides Deep Learning-based temporal super-resolution for fast bioimaging. It increases the frame rate of any microscope modality by interpolating an image in between two consecutive images via “intelligent” interpolation, providing 2x increase in temporal resolution.
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!
Thread 0/7
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!
Thread 0/7
Thread 1/7
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)!
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)!
Thread 2/7
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 !
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 !
