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{
"schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json",
"version": "0.5.1",
"changelog": {
"0.5.1": "enhance metadata with improved descriptions and task specification",
"0.5.0": "update to huggingface hosting and fix missing dependencies",
"0.4.9": "use monai 1.4 and update large files",
"0.4.8": "update to use monai 1.3.1",
"0.4.7": "add load_pretrain flag for infer",
"0.4.6": "add output for inference",
"0.4.5": "update with EnsureChannelFirstd and remove meta dict usage",
"0.4.4": "fix the wrong GPU index issue of multi-node",
"0.4.3": "add dataset dir example",
"0.4.2": "update ONNX-TensorRT descriptions",
"0.4.1": "update the model weights with the deterministic training",
"0.4.0": "add the ONNX-TensorRT way of model conversion",
"0.3.9": "fix mgpu finalize issue",
"0.3.8": "enable deterministic training",
"0.3.7": "adapt to BundleWorkflow interface",
"0.3.6": "add name tag",
"0.3.5": "fix a comment issue in the data_process script",
"0.3.4": "add note for multi-gpu training with example dataset",
"0.3.3": "enhance data preprocess script and readme file",
"0.3.2": "restructure readme to match updated template",
"0.3.1": "add workflow, train loss and validation accuracy figures",
"0.3.0": "update dataset processing",
"0.2.2": "update to use monai 1.0.1",
"0.2.1": "enhance readme on commands example",
"0.2.0": "update license files",
"0.1.0": "complete the first version model package",
"0.0.1": "initialize the model package structure"
},
"monai_version": "1.4.0",
"pytorch_version": "2.4.0",
"numpy_version": "1.24.4",
"required_packages_version": {
"nibabel": "5.2.1",
"pytorch-ignite": "0.4.11",
"pillow": "10.4.0",
"tensorboard": "2.17.0"
},
"supported_apps": {},
"name": "Endoscopic In-Body Classification",
"task": "Endoscopic Frame Classification for In-Body vs Out-Body Detection",
"description": "A binary classification model based on SENet that distinguishes between inside-body and outside-body frames in endoscopic videos. The model processes 256x256 pixel RGB images and filters irrelevant frames, enabling automated procedure analysis.",
"authors": "MONAI team",
"copyright": "Copyright (c) MONAI Consortium",
"data_source": "private dataset",
"data_type": "RGB",
"image_classes": "three channel data, intensity [0-255]",
"label_classes": "0: inbody, 1: outbody",
"pred_classes": "vector whose length equals to 2, [1,0] means in body, [0,1] means out body",
"eval_metrics": {
"accuracy": 0.99
},
"intended_use": "This is a research tool/prototype and not to be used clinically",
"references": [
"J. Hu, L. Shen and G. Sun, Squeeze-and-Excitation Networks, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, pp. 7132-7141. https://arxiv.org/pdf/1709.01507.pdf"
],
"network_data_format": {
"inputs": {
"image": {
"type": "magnitude",
"format": "RGB",
"modality": "regular",
"num_channels": 3,
"spatial_shape": [
256,
256
],
"dtype": "float32",
"value_range": [
0,
1
],
"is_patch_data": false,
"channel_def": {
"0": "R",
"1": "G",
"2": "B"
}
}
},
"outputs": {
"pred": {
"type": "probabilities",
"format": "classes",
"num_channels": 2,
"spatial_shape": [
1,
2
],
"dtype": "float32",
"value_range": [
0,
1
],
"is_patch_data": false,
"channel_def": {
"0": "in body",
"1": "out body"
}
}
}
}
}
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