CNN regression hyperparameter optimizer

CNN regression tool built to identify optimal network parameters using cross validation, includes image augmentation and random oversampling
194 Downloads
Updated 8 Mar 2022

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Deep learning convolutional neural network regression
With network parameter gridsearch, input normalization and geometric image augmenation:
Network is designed to learn to predict a numerical value from images
Provided example input (X)data is 3 channel (R2*, QSM, GRE mag) MR image slices of ex vivo stroke blood clots
Provided example output (Y)data is RBC content of clots determined through histological analysis
Built to iterate cross-validation experiments over a set of specified network parameters (gridsearch)
Can customize which parameters to iterate over by changing the for loops
Network settings which produce the highest accuracy will be used to form predictions on a separate test set
Network can normalize training data input based on distribution of the output variable using random oversampling (ROS)
Network can apply random geometric augmentation operations to training data
And duplicate training dataset to increase size, prior to augmenation (duplication factor)
Handles input predictor (x) data as [X, Y, nchannel, nslice] where nchannel = 1 or 3
Handles input prediction (y) data as [y(1):y(nslice)]

Cite As

Spencer D. Christiansen (2024). CNN regression hyperparameter optimizer (https://www.mathworks.com/matlabcentral/fileexchange/100661-cnn-regression-hyperparameter-optimizer), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2020b
Compatible with R2020b and later releases
Platform Compatibility
Windows macOS Linux

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Version Published Release Notes
1.1

-fixed overwrite_crossval_results_table glitch that created new table for every iteration
-fixed nonexistent aug_params matrix when augment_training_data set to 0
-minor changes to comments and variable names for clarity

1.0.1