Comments (3)
agreed, pytorch tutorials use these categories
from tutorials.
As well as categories, it might be a good idea to start with e.g., nifti_read_example.ipynb
and 3d_image_transforms.ipynb
to get a feel for some building blocks before moving into whole networks.
from tutorials.
the codebase is now restructured, I'm closing this ticket, please open new ones for further issues
from tutorials.
Related Issues (20)
- module 'cv2.dnn' has no attribute 'DictValue'
- ImportError: libGL.so.1: cannot open shared object file: No such file or directory
- Kernel hangs in "TCIA_PROSTATEx_Prostate_MRI_Anatomy_Model.ipynb" HOT 11
- nbclient.exceptions.DeadKernelError: Kernel died in ./modules/3d_image_transforms.ipynb HOT 7
- Issue with multi-GPU support in Auto3DSeg on Windows HOT 1
- the argument needed to change the default directory in pathology/tumor_detection/README.MD
- please upload more famous diffusion model about image to image,thanks HOT 1
- Certificate verify failed when downloading OASIS data
- monailabel tutorials contain broken and outdated links to Orthanc HOT 2
- IndexError in modules/resample_benchmark.ipynb HOT 1
- Auto3DSeg to test data for segmentation of the Lung HOT 11
- The execution of `runner.sh` is timing out due to the large dataset
- Breakup of datasets used during lung nodule detector training HOT 3
- Create tutorial for 3d regression.
- Auto3DSeg evaluation using dice coefficient
- Adding tutorial to demonstrate how to train a generative model that can generate large image volumes
- The monai-generative in requirements-dev.txt points to a deleted branch.
- Maisi debug HOT 1
- can't run on large datasets HOT 2
- Issue in maisi_train_vae_tutorial HOT 2
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from tutorials.