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README.md
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A pre-trained model for volumetric (3D) segmentation of the spleen from CT image.
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# Model Overview
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This model is trained using the
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## Data
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The training dataset is Task09_Spleen.tar from http://medicaldecathlon.com/.
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Output: 2 channels: Label 1: spleen; Label 0: everything else
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## Scores
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This model
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Mean
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## commands example
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Execute inference:
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Verify the metadata format:
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Verify the data shape of network:
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Export checkpoint to TorchScript file:
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# Disclaimer
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This is an example, not to be used for diagnostic purposes.
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A pre-trained model for volumetric (3D) segmentation of the spleen from CT image.
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# Model Overview
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This model is trained using the runner-up [1] awarded pipeline of the "Medical Segmentation Decathlon Challenge 2018" using the UNet architecture [2] with 32 training images and 9 validation images.
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## Data
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The training dataset is Task09_Spleen.tar from http://medicaldecathlon.com/.
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Output: 2 channels: Label 1: spleen; Label 0: everything else
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## Scores
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This model achieves the following Dice score on the validation data (our own split from the training dataset):
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Mean Dice = 0.96
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## commands example
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Execute inference:
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```
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python -m monai.bundle run evaluator --meta_file configs/metadata.json --config_file configs/inference.json --logging_file configs/logging.conf
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```
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Verify the metadata format:
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```
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python -m monai.bundle verify_metadata --meta_file configs/metadata.json --filepath eval/schema.json
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```
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Verify the data shape of network:
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```
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python -m monai.bundle verify_net_in_out network_def --meta_file configs/metadata.json --config_file configs/inference.json
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```
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Export checkpoint to TorchScript file:
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```
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python -m monai.bundle export network_def --filepath models/model.ts --ckpt_file models/model.pt --meta_file configs/metadata.json --config_file configs/inference.json
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```
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# Disclaimer
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This is an example, not to be used for diagnostic purposes.
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