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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": "enhance 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": "define arg for output file and put infer logic into a function",
"1.0.7": "update AddChanneld 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": "remove unused saver in inference",
"1.0.1": "fix inference folder error",
"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",
"tensorboard": "2.17.0",
"einops": "0.7.0",
"pytorch-ignite": "0.4.11"
},
"supported_apps": {},
"name": "BraTS MRI Axial Slices Latent Diffusion Generation",
"task": "Conditional Synthesis of Brain MRI Axial Slices",
"description": "Latent diffusion model that synthesizes 2D brain MRI axial slices (240x240 pixels) from Gaussian noise, trained on the BraTS dataset. The model processes 1-channel latent space features (64x64) and generates FLAIR sequences with 1mm in-plane resolution, capturing diverse tumor and brain tissue appearances.",
"authors": "MONAI team",
"copyright": "Copyright (c) MONAI Consortium",
"data_source": "http://medicaldecathlon.com/",
"data_type": "nibabel",
"image_classes": "Flair brain MRI axial slices with 1x1 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": [
240,
240
],
"dtype": "float32",
"value_range": [
0,
1
],
"is_patch_data": true
}
},
"outputs": {
"pred": {
"type": "image",
"format": "image",
"num_channels": 1,
"spatial_shape": [
240,
240
],
"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": 1,
"spatial_shape": [
64,
64
],
"dtype": "float32",
"value_range": [
0,
1
],
"is_patch_data": true
}
},
"outputs": {
"pred": {
"type": "feature",
"format": "image",
"num_channels": 1,
"spatial_shape": [
64,
64
],
"dtype": "float32",
"value_range": [
0,
1
],
"is_patch_data": true,
"channel_def": {
"0": "image"
}
}
}
}
}
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