Versatile Deep Learning for 2D and 3D Bioimage Analysis with CytoDL

Versatile Deep Learning for 2D and 3D Bioimage Analysis with CytoDL | Benjamin Morris | October 30th, Virtual I2K 2024
Authors: Matheus Viana, Allen Institute for Cell Science; Benji Morris, Allen Institute for Cell Science
Description:
Target Audience: Biologists, microscopists, and researchers with basic Python experience interested in applying deep learning to their image data.
Data scientists and machine learning engineers seeking to expand their knowledge in bioimage analysis.
Workshop Description: This hands-on workshop will introduce you to CytoDL, a powerful deep learning framework developed by the Allen Institute for Cell Science. CytoDL is designed to streamline the analysis of biological images, including 2D and 3D data represented as images, point clouds, and tabular formats.
You will learn how to leverage CytoDL to:
Get single cell and nucleus instance segmentations from image datasets from the Allen Institute from Cell Science [1, 2];
Use the single cell images from (A) to extract unsupervised features to detect morphological perturbations of intracellular structures [2].
[1] - Viana, Matheus P., et al. "Integrated intracellular organization and its variations in human iPS cells." Nature 613.7943 (2023): 345-354.
[2] - Donovan-Maiye, Rory M., et al. "A deep generative model of 3D single-cell organization." PLOS Computational Biology 18.1 (2022): e1009155.
Keywords: Deep Learning, Segmentation, Instance Segmentation, Contrastive Learning, Perturbation Detection