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Introduction
Exploring the Intersection of Deconvolution, GPU Computing, and Deep Learning
Module 1: Course preparation
Mambaforge and devbio-napari
Dependencies
Get test data
Test deconvolution imports
Test deep learning libraries
Module 2: Deconvolution
Forward modelling
Nuclei Deconvolution and Compare intensities to ground truth
A Notebook showing ‘reverse Deconvolution’ a.k.a. PSF Distilling
Deconvolution Systems Design
Module3: Deep Learning
Creating training data
Creating training data from cytopacq simulated nuclei images
Train and restore with trivial conv net
Train a CARE conv net
Care restoration (prediction)
Train a stardist neural network for segmentation
Segment with stardist model
Collaborative Assignment
Second set of notes
Repository
Open issue
.md
.pdf
Second set of notes
Second set of notes
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More notes…
