monai
medical
katielink commited on
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c35c6d7
1 Parent(s): c0c50b7

update required packages

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  1. README.md +0 -3
  2. configs/metadata.json +5 -3
  3. docs/README.md +0 -3
README.md CHANGED
@@ -26,9 +26,6 @@ An example result from inference is shown below:
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  **This is a demonstration network meant to just show the training process for this sort of network with MONAI. To achieve better performance, users need to use larger dataset like [Brats 2021](https://www.synapse.org/#!Synapse:syn25829067/wiki/610865) and have GPU with memory larger than 32G to enable larger networks and attention layers.**
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- ## MONAI Generative Model Dependencies
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- This bundle requires to install [MONAI generative models](https://github.com/Project-MONAI/GenerativeModels).
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-
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  ## Data
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  The training data is BraTS 2016 and 2017 from the Medical Segmentation Decathalon. Users can find more details on the dataset (`Task01_BrainTumour`) at http://medicaldecathlon.com/.
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  **This is a demonstration network meant to just show the training process for this sort of network with MONAI. To achieve better performance, users need to use larger dataset like [Brats 2021](https://www.synapse.org/#!Synapse:syn25829067/wiki/610865) and have GPU with memory larger than 32G to enable larger networks and attention layers.**
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  ## Data
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  The training data is BraTS 2016 and 2017 from the Medical Segmentation Decathalon. Users can find more details on the dataset (`Task01_BrainTumour`) at http://medicaldecathlon.com/.
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configs/metadata.json CHANGED
@@ -1,17 +1,19 @@
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  {
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  "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_generator_ldm_20230507.json",
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- "version": "1.0.2",
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  "changelog": {
 
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  "1.0.2": "unify dataset dir in different configs",
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  "1.0.1": "update dependency, update trained model weights",
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  "1.0.0": "Initial release"
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  },
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- "monai_version": "1.2.0rc5",
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  "pytorch_version": "1.13.1",
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  "numpy_version": "1.22.2",
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  "optional_packages_version": {
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  "nibabel": "5.1.0",
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- "lpips": "0.1.4"
 
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  },
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  "name": "BraTS MRI image latent diffusion generation",
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  "task": "BraTS MRI image synthesis",
 
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  {
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  "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_generator_ldm_20230507.json",
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+ "version": "1.0.3",
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  "changelog": {
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+ "1.0.3": "update required packages",
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  "1.0.2": "unify dataset dir in different configs",
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  "1.0.1": "update dependency, update trained model weights",
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  "1.0.0": "Initial release"
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  },
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+ "monai_version": "1.2.0rc7",
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  "pytorch_version": "1.13.1",
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  "numpy_version": "1.22.2",
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  "optional_packages_version": {
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  "nibabel": "5.1.0",
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+ "lpips": "0.1.4",
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+ "monai-generative": "0.2.2"
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  },
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  "name": "BraTS MRI image latent diffusion generation",
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  "task": "BraTS MRI image synthesis",
docs/README.md CHANGED
@@ -19,9 +19,6 @@ An example result from inference is shown below:
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  **This is a demonstration network meant to just show the training process for this sort of network with MONAI. To achieve better performance, users need to use larger dataset like [Brats 2021](https://www.synapse.org/#!Synapse:syn25829067/wiki/610865) and have GPU with memory larger than 32G to enable larger networks and attention layers.**
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- ## MONAI Generative Model Dependencies
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- This bundle requires to install [MONAI generative models](https://github.com/Project-MONAI/GenerativeModels).
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-
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  ## Data
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  The training data is BraTS 2016 and 2017 from the Medical Segmentation Decathalon. Users can find more details on the dataset (`Task01_BrainTumour`) at http://medicaldecathlon.com/.
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  **This is a demonstration network meant to just show the training process for this sort of network with MONAI. To achieve better performance, users need to use larger dataset like [Brats 2021](https://www.synapse.org/#!Synapse:syn25829067/wiki/610865) and have GPU with memory larger than 32G to enable larger networks and attention layers.**
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  ## Data
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  The training data is BraTS 2016 and 2017 from the Medical Segmentation Decathalon. Users can find more details on the dataset (`Task01_BrainTumour`) at http://medicaldecathlon.com/.
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