Machine Learning & Interactive Image Processing with Dash
Featuring Emmanuelle Gouillart, maintainer of Scikit-Image and Plotly scientist-in-residence
With its flexible Python framework, Dash is the platform of choice for machine learning scientists wanting to build deep learning models. Dash enables the use of off-the-shelf algorithms and estimators from PyData packages like scikit-image, scikit-learn or pytorch, which are popular for image processing. Join Emmanuelle Gouillart, Plotly scientist-in-residence, as she showcases her work with open-source Dash for the Chan-Zuckerberg Initiative. In this 1-hour webinar and AMA, she will show how to use image annotations and machine learning in Dash for interactive image processing.
In this recorded session, you will learn how to do the following:
- Generate and implement image annotations in Dash apps (bounding boxes, closed and open contours, overlays, etc.)
- Use scikit-image and scikit-learn for building training sets or seeding segmentation algorithms
- Build a 3D image partitioning and image segmentation interactive Dash app
- Operationalize image processing tools for the medical and scientific community
About the Speaker
Emmanuelle is a maintainer of Scikit-Image, Dash-canvas and plotly.py open-source libraries and a leader in the Scientific Python community. She has a PhD in Fluid Dynamics from Université Pierre et Marie Curie. At Plotly, she is leading the "Interactive image processing with Scikit-Image and Dash" open-source project funded by the Chan-Zuckerberg Initiative and its Essential Open-Source Software for Science program.