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Signed-off-by: Daniël Nobbe <[email protected]>
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auto3dseg/README.md

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@@ -58,14 +58,15 @@ To further demonstrate the capabilities of **Auto3DSeg**, [here](./tasks/instanc
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## Running With Your Own Data
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To run Auto3DSeg on your own dataset, all you need to do is build a `datalist.json` file for your dataset, and run the AutoRunner on it.
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To run Auto3DSeg on your own dataset, you need to build a `datalist.json` file for your dataset, and run the AutoRunner on it.
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The datalist format is based on the datasets released by the (Medical Segmentation Decathlon)[http://medicaldecathlon.com].
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The datalist format is based on the datasets released by the [Medical Segmentation Decathlon](http://medicaldecathlon.com).
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See the function `load_decathlon_datalist` in `monai/data/decathlon_datalist.py` for a description of the format.
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For the AutoRunner, we only need the `training` data, since it will automatically create cross-validation folds.
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You are free to add the cross-validation folds beforehand, these should align with the number of folds set in the configuration of the AutoRunner (by default 5, see [notebook](notebooks/auto_runner.ipynb)).
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Any other metadata, such as `modality`, `numTraining`, `name`, etc. will not be used by the AutoRunner, but we do recommend adding them, to keep track of names and versions of the dataset.
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For the AutoRunner, we only need the `training` list in the JSON, it does not use any other fields.
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The `fold` key for each image is not required, as the AutoRunner will automatically create cross-validation folds.
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If you do add the cross-validation folds beforehand, these should align with the number of folds set in the configuration of the AutoRunner (by default 5, see [notebook](notebooks/auto_runner.ipynb)).
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Any other metadata, such as `modality`, `numTraining`, `name`, etc. will not be used by the AutoRunner, but we do recommend using metadata fields to keep track of names and versions of your dataset.
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In short, your `datalist.json` file should look like this:
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```
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```
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The AutoRunner will create a `work_dir` folder in the directory from which it is ran, with the resulting models and the copied datalist file _with_ cross-validation folds. This allows you to see which datalist file the models are trained on.
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The AutoRunner will create a `work_dir` folder in the directory from which it is ran, which will contain the resulting models and the copied datalist file _with_ cross-validation folds. This allows you to keep track of which datalist file the models are trained on.
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See the description below or the file [run_with_minimal_input.md](docs/run_with_minimal_input.md) how to use your datalist with the AutoRunner.
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See the description below or the file [run_with_minimal_input.md](docs/run_with_minimal_input.md) to use your datalist with the AutoRunner.
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## Reference Python APIs for Auto3DSeg
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