Startseite Erkunden Hilfe
Anmelden
nschense
/
alzheimers_nn
1
0
Fork 0
Dateien Issues 0 Pull-Requests 0 Wiki

Keine Beschreibung

Struktur: 72c64b23d9
Branches Tags
alzheimers_nn
ZIP TAR.GZ
nschense 72c64b23d9 Update 'README.md' vor 2 Monaten
.vscode a02334abbf More work done on overall stats - not sure why so many files changed vor 8 Monaten
utils a02334abbf More work done on overall stats - not sure why so many files changed vor 8 Monaten
.gitignore a02334abbf More work done on overall stats - not sure why so many files changed vor 8 Monaten
LP_ADNIMERGE.csv a02334abbf More work done on overall stats - not sure why so many files changed vor 8 Monaten
README.md 72c64b23d9 Update 'README.md' vor 2 Monaten
bayesian.py a02334abbf More work done on overall stats - not sure why so many files changed vor 8 Monaten
config.toml 300dc9dcf4 Update 'config.toml' in preparation for sharing vor 2 Monaten
ensemble_predict.py a02334abbf More work done on overall stats - not sure why so many files changed vor 8 Monaten
ruff.toml a02334abbf More work done on overall stats - not sure why so many files changed vor 8 Monaten
sensitivity_analysis.py a02334abbf More work done on overall stats - not sure why so many files changed vor 8 Monaten
threshold.py 1083b2301b Completed refactor to xarray - working on minimial calibration and sensitivity analysis vor 8 Monaten
threshold_refac.py 1083b2301b Completed refactor to xarray - working on minimial calibration and sensitivity analysis vor 8 Monaten
threshold_xarray.py 1083b2301b Completed refactor to xarray - working on minimial calibration and sensitivity analysis vor 8 Monaten
train_cnn.py a02334abbf More work done on overall stats - not sure why so many files changed vor 8 Monaten

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!