Speaker
Description
The clEsperanto project equips users and developers with GPU-accelerated image-processing components. It ships a library with which you will be able to build fast bioImage analysis scripts, by leveraging modern GPUs.
Positioned as a classical Image Processing library, it is built on a framework that makes it available in many language (Python, Java, C++), in many software platforms (Fiji, Napari, Icy), and is compatible with any GPU brand and model. Additionally, all implementation adopt a standardized naming convention and integrate seamlessly with graphical user interfaces for the Fiji and Napari platforms, further reducing the entry barrier. Collectively, these features enhance reusability, reproducibility, and maintainability, significantly lowering the learning curve for GPU-accelerated BioImage Analysis for both users and developers.
The workshop will offer an overview of the library, thoroughly covering its features, and will also provide an introduction to GPU hardware and its use. Participants will then be guided through the usage of the library in Python, with practical examples and real-world use cases. The workshop will conclude with a demonstration of the library's application in Fiji and Napari.
You should attend if your job involves building image analysis pipelines. Participants should have a basic understanding of Python and have conda installed on their computer to fully engage with the workshop.
| Target audience | Intermediate users, Advanced users, Beginner developers |
|---|---|
| Keywords | Image Processing, GPU, Python, Java, Fiji, Napari |
