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{
"schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json",
"version": "1.1.4",
"changelog": {
"1.1.4": "enhanced metadata with improved descriptions and task specification",
"1.1.3": "update to huggingface hosting and fix missing dependencies",
"1.1.2": "update issue for IgniteInfo",
"1.1.1": "enable tensorrt",
"1.1.0": "update to use monai 1.4, model ckpt not changed, rm GenerativeAI repo",
"1.0.9": "update to use monai 1.3.1",
"1.0.8": "update run section",
"1.0.7": "update with EnsureChannelFirstd",
"1.0.6": "update with new lr scheduler api in inference",
"1.0.5": "fix the wrong GPU index issue of multi-node",
"1.0.4": "update with new lr scheduler api",
"1.0.3": "update required packages",
"1.0.2": "unify dataset dir in different configs",
"1.0.1": "update dependency, update trained model weights",
"1.0.0": "Initial release"
},
"monai_version": "1.4.0",
"pytorch_version": "2.4.0",
"numpy_version": "1.24.4",
"required_packages_version": {
"nibabel": "5.2.1",
"lpips": "0.1.4",
"einops": "0.7.0",
"pytorch-ignite": "0.4.11",
"tensorboard": "2.17.0"
},
"supported_apps": {},
"name": "BraTS MRI Latent Diffusion Generation",
"task": "Conditional Synthesis of Brain MRI with Tumor Features",
"description": "Volumetric latent diffusion model that generates 3D brain MRI volumes (112x128x80 voxels) with tumor features from Gaussian noise, trained on the BraTS multimodal MRI dataset.",
"authors": "MONAI team",
"copyright": "Copyright (c) MONAI Consortium",
"data_source": "http://medicaldecathlon.com/",
"data_type": "nibabel",
"image_classes": "Flair brain MRI with 1.1x1.1x1.1 mm voxel size",
"eval_metrics": {},
"intended_use": "This is a research tool/prototype and not to be used clinically",
"references": [],
"autoencoder_data_format": {
"inputs": {
"image": {
"type": "image",
"format": "image",
"num_channels": 1,
"spatial_shape": [
112,
128,
80
],
"dtype": "float32",
"value_range": [
0,
1
],
"is_patch_data": true
}
},
"outputs": {
"pred": {
"type": "image",
"format": "image",
"num_channels": 1,
"spatial_shape": [
112,
128,
80
],
"dtype": "float32",
"value_range": [
0,
1
],
"is_patch_data": true,
"channel_def": {
"0": "image"
}
}
}
},
"network_data_format": {
"inputs": {
"latent": {
"type": "noise",
"format": "image",
"num_channels": 8,
"spatial_shape": [
36,
44,
28
],
"dtype": "float32",
"value_range": [
0,
1
],
"is_patch_data": true
},
"condition": {
"type": "timesteps",
"format": "timesteps",
"num_channels": 1,
"spatial_shape": [],
"dtype": "long",
"value_range": [
0,
1000
],
"is_patch_data": false
}
},
"outputs": {
"pred": {
"type": "feature",
"format": "image",
"num_channels": 8,
"spatial_shape": [
36,
44,
28
],
"dtype": "float32",
"value_range": [
0,
1
],
"is_patch_data": true,
"channel_def": {
"0": "image"
}
}
}
}
}
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