Here, we cover the GPU-Accelerated Image Analysis Track of the PoL Bio-Image Analysis Symposium
- clesperanto
- cupy
- dask
- Deconvolution
- Pytorch
- AI-based Denoising
- AI-based Segmentation
This work is licensed by Stephane Rigaud, Brian Northan, Till Korten, Neringa Jurenaite, Apurv Deepak Kulkarni, Peter Steinbach, Sebastian Starke, Johannes Soltwedel, Marvin Albert and Robert Haase under a Creative Commons Attribution 4.0 International License.
This repository hosts notebooks, information and data for the GPU-Accelerated Image Analysis Track of the PoL Bio-Image Analysis Symposium.
https://biapol.github.io/PoL-BioImage-Analysis-TS-GPU-Accelerated-Image-Analysis/
It is maintained using Jupyter lab and build using Jupyter book.
This course was held in Dresden, August 2023. We would like to thank all the people who shared teaching materials we are reusing here. We acknowledge support by the Deutsche Forschungsgemeinschaft under Germany’s Excellence Strategy—EXC2068–Cluster of Excellence Physics of Life of TU Dresden. This project has been made possible in part by grant number 2021-237734 (GPU-accelerating Fiji and friends using distributed CLIJ, NEUBIAS-style, EOSS4) from the Chan Zuckerberg Initiative DAF, an advised fund of the Silicon Valley Community Foundation.