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Pre-training on large-scale T2w healthy/pathology data #11

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@naga-karthik naga-karthik commented Mar 24, 2024

This PR adds the code to do supervised (and self-supervised) pretraining on T2w MRI data from a collection of public/private, healthy/pathology datasets.

TODO:

  • Add code for self-supervised pre-training
  • Test single-gpu and multi-gpu training

valosekj and others added 30 commits March 12, 2024 17:05
It seems that the transform randomly selects a foreground point from image, then use it as center crop. This means that it can find the closest voxel that is just outside the SC and use it as the center (hence it includes the SC).
Source: Project-MONAI/MONAI#452 (comment)
…or the RandCoarseDropoutd and RandCoarseShuffled transforms
number of holes to be used for the RandCoarseDropoutd and RandCoarseShuffled transforms
From contrast-agnostic SC seg transforms (which heavily deform the image) to simpler transforms more suitable for lesion seg.
Context: #10 (comment)

Co-authored-by: Naga Karthik Enamundram <[email protected]>
From MONAI doc (https://docs.monai.io/en/latest/transforms.html#randcoarsedropoutd):
- if True, dropout the regions of holes and fill value specified by 'fill_value'
- if False, keep the holes and dropout the outside and fill value specified by 'fill_value'
… are using two augmented samples per 3D Volume
naga-karthik and others added 30 commits April 8, 2024 16:14
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2 participants