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resnet18, resnet50, densenet121hipvascai_monai_classification_model.zipimagesTslabelsTs may be used for ROI cropping_0000.nii.gz: precontrast MRI_0001.nii.gz: subtraction MRIclass_0class_1class_2class_3[128, 128, 32]. When no mask is available, inference uses the full image volume according to the pipeline defaults.1python 3dclassification_inference\run_inference.py `
2 --data-dir X:\path\to\my_inference_cases `
3 --scratch-dir C:\scratch\hipvascai_classification_inference `
4 --model-repo vishalgokani/hipvascai-classification `
5 --models resnet18 resnet50 densenet121 `
6 --batch-size 11python 3dclassification_inference\run_inference.py `
2 --data-dir X:\path\to\my_inference_cases `
3 --scratch-dir C:\scratch\hipvascai_classification_inference `
4 --model-zip C:\path\to\hipvascai_monai_classification_model.zip `
5 --models resnet18 resnet50 densenet121 `
6 --batch-size 11my_inference_cases/
2 imagesTs/
3 case_001_0000.nii.gz
4 case_001_0001.nii.gz
5 labelsTs/
6 case_001.nii.gzlabelsTs is optional. Outputs are written back to my_inference_cases/hipvascai_monai_classification_results/.1hipvascai_monai_classification_model/
2 training/
3 resnet18/
4 final_average_5fold_model.pt
5 cross_validation_summary.json
6 fold_summary.csv
7 resnet50/
8 final_average_5fold_model.pt
9 cross_validation_summary.json
10 fold_summary.csv
11 densenet121/
12 final_average_5fold_model.pt
13 cross_validation_summary.json
14 fold_summary.csv
15 model_comparison_summary.json
16 training_manifest.json
17 model_manifest.json