Comments (2)
Hi @sairampillai. Thanks for your interest!
Yes, this might be a good one. I had in mind something like what I did for NiftyNet:
You can start by
- Fork this repo
- Create a branch
48-add-antialiasing
on the fork - Commit your changes to that branch
- Submit a pull request to this repo, even if it's not ready
I'll review the commits and merge your changes.
Thank you!
from torchio.
Hey @fepegar. I would like to contribute. Would this be a good issue to start with? Any other details regarding this issue?
from torchio.
Related Issues (20)
- Suggestions the modifying default value of prefetch_factor and the argument to set it for minimize the blocking-bottleneck between fetch subject and generate patch in Queue HOT 1
- Different transforms applied to CT and label HOT 11
- The Affine matrix does not change after applying the augmentations HOT 3
- Custom loader not used when loading data lazily HOT 2
- Seed is not working HOT 2
- Silenced exception makes it harder to debug custom Transforms HOT 5
- Resample
- tio.Resample does not work with custom image class HOT 2
- Setting NUM_SAMPLES when using sampler with Queue HOT 3
- RescaleIntensity - multiple calls HOT 9
- Return sampled parameters upon request HOT 3
- Halve queue length when using DDP HOT 2
- bug in rotation part of tio.transforms.RandomAffine HOT 4
- get_subjects_from_batch has a hick-up with int metadata HOT 5
- masking_method in Mask class is not saved as argument (preventing applying the inverse transform)
- RandomAffine raises an error when isotropic=True and 3 elements are given for scales HOT 10
- Queue is not respecting the batch size HOT 1
- Resample an image by providing only the target affine HOT 1
- Supporting PyTorch 2.3 HOT 17
- Cannot copy subclass of Subject with keyword arguments HOT 2
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from torchio.