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Nicholas Schense 11ea1d0a02 Merge branch 'system-rewrite' of https://git0.fmf.uni-lj.si/nschense/alzheimers_nn into system-rewrite hace 7 meses
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README.md 72c64b23d9 Update 'README.md' hace 9 meses
bayesian.py a02334abbf More work done on overall stats - not sure why so many files changed hace 1 año
calibration_xarray.py 4549b2b349 Worked on calibration (little success) and began tweaking graphs for presentation and poster hace 1 año
config.toml 300dc9dcf4 Update 'config.toml' in preparation for sharing hace 9 meses
dataset_size.py 26e4e9c3f3 Commit of work from summer hace 7 meses
ensemble_predict.py a02334abbf More work done on overall stats - not sure why so many files changed hace 1 año
ruff.toml a02334abbf More work done on overall stats - not sure why so many files changed hace 1 año
sensitivity_analysis.py a02334abbf More work done on overall stats - not sure why so many files changed hace 1 año
threshold.py 1083b2301b Completed refactor to xarray - working on minimial calibration and sensitivity analysis hace 1 año
threshold_refac.py 1083b2301b Completed refactor to xarray - working on minimial calibration and sensitivity analysis hace 1 año
threshold_xarray.py 26e4e9c3f3 Commit of work from summer hace 7 meses
train_cnn.py a02334abbf More work done on overall stats - not sure why so many files changed hace 1 año
xarray_images.py 26e4e9c3f3 Commit of work from summer hace 7 meses
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README.md

Alzheimers Diagnosis Neural Net Project Rewrite

This code is the current version for the Alzheimers CNN uncertanity estimation project. The project consists of a set of main scripts (in the root folder) and some utilities. In order to use the project:

  1. Edit "config.toml" with the details of the ensemble that you would like to train (size, name, epochs etc). Make sure that the model name and ensemble name are the same if you'd like to run the ensemble analysis later.
  2. Run "train_cnn.py". This will train and save a new ensemble of CNN models using the name and congfiguration options given in the config file.
  3. Run "ensemble_predict.py". This will generate the predictions of the models on the test and validation datasets and save them to the model ensemble folder.
  4. Run "threshold_xarray.py". This run some analysis on the ensemble and generates a set of graphs and statistics.

"bayesian.py" is unfinished and does not currently work. The other two threshold files are old implementations. 'sensitivity_analysis.py' can be optionally used to generate some model number sensitivity data, but the implementation is buggy currently. Apologies for the messy code throughout!