Merge remote-tracking branch 'mrokuss/master'

documentation/pretraining_and_finetuning.md

@@ -2,7 +2,7 @@
 
 ## Intro
 
-So far nnU-Net only supports supervised pre-training, meaning that you train a regular nnU-Net on some source dataset 
+So far nnU-Net only supports supervised pre-training, meaning that you train a regular nnU-Net on some pretraining dataset 
 and then use the final network weights as initialization for your target dataset. 
 
 As a reminder, many training hyperparameters such as patch size and network topology differ between datasets as a 
@@ -16,11 +16,11 @@ how the resulting weights can then be used for initialization.
 
 Throughout this README we use the following terminology:
 
-- `source dataset` is the dataset you intend to run the pretraining on
+- `pretraining dataset` is the dataset you intend to run the pretraining on (former: source dataset)
 - `target dataset` is the dataset you are interested in; the one you wish to fine tune on
 
 
-## Pretraining on the source dataset
+## Training on the pretraining dataset
 
 In order to obtain matching network topologies we need to transfer the plans from one dataset to another. Since we are 
 only interested in the target dataset, we first need to run experiment planning (and preprocessing) for it:
@@ -29,19 +29,19 @@ only interested in the target dataset, we first need to run experiment planning
 nnUNetv2_plan_and_preprocess -d TARGET_DATASET
 ```
 
-Then we need to extract the dataset fingerprint of the source dataset, if not yet available:
+Then we need to extract the dataset fingerprint of the pretraining dataset, if not yet available:
 
 ```bash
-nnUNetv2_extract_fingerprint -d SOURCE_DATASET
+nnUNetv2_extract_fingerprint -d PRETRAINING_DATASET
 ```
 
-Now we can take the plans from the target dataset and transfer it to the source:
+Now we can take the plans from the target dataset and transfer it to the pretraining dataset:
 
 ```bash
-nnUNetv2_move_plans_between_datasets -s TARGET_DATASET -t SOURCE_DATASET -sp TARGET_PLANS_IDENTIFIER -tp SOURCE_PLANS_IDENTIFIER
+nnUNetv2_move_plans_between_datasets -s PRETRAINING_DATASET -t TARGET_DATASET -sp PRETRAINING_PLANS_IDENTIFIER -tp TARGET_PLANS_IDENTIFIER
 ```
 
-`SOURCE_PLANS_IDENTIFIER` is hereby probably nnUNetPlans unless you changed the experiment planner in 
+`PRETRAINING_PLANS_IDENTIFIER` is hereby probably nnUNetPlans unless you changed the experiment planner in 
 nnUNetv2_plan_and_preprocess. For `TARGET_PLANS_IDENTIFIER` we recommend you set something custom in order to not 
 overwrite default plans.
 
@@ -51,16 +51,16 @@ work well (but it could, depending on the schemes!).
 
 Note on CT normalization: Yes, also the clip values, mean and std are transferred!
 
-Now you can run the preprocessing on the source task:
+Now you can run the preprocessing on the pretraining dataset:
 
 ```bash
-nnUNetv2_preprocess -d SOURCE_DATSET -plans_name TARGET_PLANS_IDENTIFIER
+nnUNetv2_preprocess -d PRETRAINING_DATASET -plans_name TARGET_PLANS_IDENTIFIER
 ```
 
 And run the training as usual:
 
 ```bash
-nnUNetv2_train SOURCE_DATSET CONFIG all -p TARGET_PLANS_IDENTIFIER
+nnUNetv2_train PRETRAINING_DATASET CONFIG all -p TARGET_PLANS_IDENTIFIER
 ```
 
 Note how we use the 'all' fold to train on all available data. For pretraining it does not make sense to split the data.