ZeroCostDL4Mic allows the use of popular Deep Learning neural networks capable of carrying out tasks such as image segmentation and object detection (using U-Net, StarDist and YOLOv2), image denoising and restoration (using CARE and, Noise2Void), super-resolution microscopy (using Deep-STORM) and image-to-image translations (using Label-free prediction fnet, pix2pix and CycleGAN). With ZeroCostDL4Mic, researchers with little or no coding expertise are able to train (and re-train), validate, and use DL networks, though a browser and for free, thanks to the Google Collab engine the platform uses.
This project initiated as a collaboration with Dr Ricardo Henriques laboratory, considerably expanding with the help of laboratories spread across the planet. There is a long list of contributors associated with the project acknowledged in our preprint and the wiki page.
- ZeroCostDL4Mic bioRxiv preprint
- Tutorial explaining how it works
- Romain Laine’s talk describing ZeroCostDL4Mic
- ZeroCostDL4Mic User Gallery
- #ZeroCostDL4Mic on Twitter
Examples
