File size: 3,118 Bytes
cd6dcce 5307494 cd6dcce 5307494 774c577 b547036 560f266 9272243 0bba136 cd6dcce 560f266 cd6dcce 5307494 cd6dcce 5307494 cd6dcce |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 |
{
"schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
"version": "0.2.2",
"changelog": {
"0.2.2": "add name tag",
"0.2.1": "fix license Copyright error",
"0.2.0": "update license files",
"0.1.3": "Add training pipeline for fine-tuning models, support MONAI Label active learning",
"0.1.2": "fixed the dimension in convolution according to MONAI 1.0 update",
"0.1.1": "fixed the model state dict name",
"0.1.0": "complete the model package"
},
"monai_version": "1.0.0",
"pytorch_version": "1.10.0",
"numpy_version": "1.21.2",
"optional_packages_version": {
"nibabel": "3.2.1",
"pytorch-ignite": "0.4.8",
"einops": "0.4.1",
"fire": "0.4.0",
"timm": "0.6.7",
"torchvision": "0.11.1"
},
"name": "Renal structures UNEST segmentation",
"task": "Renal segmentation",
"description": "A transformer-based model for renal segmentation from CT image",
"authors": "Vanderbilt University + MONAI team",
"copyright": "Copyright (c) MONAI Consortium",
"data_source": "RawData.zip",
"data_type": "nibabel",
"image_classes": "single channel data, intensity scaled to [0, 1]",
"label_classes": "1: Kideny Cortex, 2:Medulla, 3:Pelvicalyceal system",
"pred_classes": "1: Kideny Cortex, 2:Medulla, 3:Pelvicalyceal system",
"eval_metrics": {
"mean_dice": 0.85
},
"intended_use": "This is an example, not to be used for diagnostic purposes",
"references": [
"Tang, Yucheng, et al. 'Self-supervised pre-training of swin transformers for 3d medical image analysis. arXiv preprint arXiv:2111.14791 (2021). https://arxiv.org/abs/2111.14791."
],
"network_data_format": {
"inputs": {
"image": {
"type": "image",
"format": "hounsfield",
"modality": "CT",
"num_channels": 1,
"spatial_shape": [
96,
96,
96
],
"dtype": "float32",
"value_range": [
0,
1
],
"is_patch_data": true,
"channel_def": {
"0": "image"
}
}
},
"outputs": {
"pred": {
"type": "image",
"format": "segmentation",
"num_channels": 4,
"spatial_shape": [
96,
96,
96
],
"dtype": "float32",
"value_range": [
0,
1
],
"is_patch_data": true,
"channel_def": {
"0": "background",
"1": "kidney cortex",
"2": "medulla",
"3": "pelvicalyceal system"
}
}
}
}
}
|