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Keep track of defected slices #24

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@abailoni abailoni commented Apr 6, 2020

Hi @constantinpape,

I cleaned up my dev branch. These are the current additions/modifications wrt master:

  • I added the possibility to keep track of artifacts added during data augmentation. I used this option to make sure that the model is trained to predict labels from the previous slice. This prevents noisy predictions, especially when a slice is completely blacked out
  • I also added the options min_distance_between_defects and nb_contiguous_artifacts to the defect augmentation
  • The shift option in RandomSlide can now be applied only to the raw image (leaving the GT in place)
  • Minor bugfix: avoid to apply twice the normalization to artifacts (only apply to the raw image once the artifacts have been applied)

abailoni and others added 30 commits February 6, 2018 15:42
# Conflicts:
#	neurofire/datasets/loader/raw.py
#	neurofire/datasets/loader/segmentation.py
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