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README.md

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How to use it

  1. $ git clone https://github.com/lunit-io/mmg-nia
  2. $ pip install -r requirements.txt
  3. $ cd data_preprocessing and do data-preprocessing here
  4. To train and test 5-fold cross validation, use $ sh test.sh $GPU_ID $PICKLE_PATH $DATA_ROOT
    e.g. $ sh test.sh 0 data_preprocessing/db/shuffled_db.pkl /data/mmg/mg_nia
    • If you want to use many GPUs, input multiple numbers: sh test.sh 0,1,2,3, ...
  5. $ cat resnet34-5fold-result
   threshold : 0.1
         calculated accuracy is 0.8144577092389047
         calculated specificity is 0.8112627121478205
         calculated sensitivity is 0.825194007255318
   threshold : 0.15
         calculated accuracy is 0.8477143885489631
         calculated specificity is 0.8706655574469052
         calculated sensitivity is 0.768865680293087
   threshold : 0.2
         calculated accuracy is 0.8648696254049115
         calculated specificity is 0.9067113501876425
         calculated sensitivity is 0.7214681631914128
   calculated auc is 0.9070296037910798
  1. $ cat densenet121-5fold-result
   threshold : 0.1
         calculated accuracy is 0.8379339405852875
         calculated specificity is 0.8391845548624011
         calculated sensitivity is 0.8327769440438639
   threshold : 0.15
         calculated accuracy is 0.8601385974141034
         calculated specificity is 0.8777076140580005
         calculated sensitivity is 0.7991593433754576
   threshold : 0.2
         calculated accuracy is 0.872767241617985
         calculated specificity is 0.9012399299294076
         calculated sensitivity is 0.7745227785230062
   calculated auc is 0.9193286916314279