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Add datasets D4, D5 and models M4, M5 #23
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Dataset D5During the M4 model training using the D4 dataset, I noticed
I checked all the D4 labels (using check_voxels.py script) and found that some labels were wrong (some labels contained values Model M5
(3 models on
|
Model M5 training has finished and saved on duke (
(figures generated using plot_nnunet_training_log.py) |
…tract epoch number and pseudo dice and plot them This is useful for comparing multi-class training (because nnUNet plots only the mean dice across classes).
@naga-karthik pointed me to this comment about TLDR: So, I retrained the model will CUDA_VISIBLE_DEVICES=2 nnUNetv2_train 012 3d_fullres all -tr nnUNetTrainer_2000epochs And indeed, the The table shows the mean +- STD Dice Score (across five testing subjects) for individual rootlets (C2-C8). |
This PR adds dataset D4 and model M4.
Dataset D4
sub-007_ses-headNormal_T2w.nii.gz
,sub-010_ses-headUp_T2w.nii.gz
,sub-amu02_T2w.nii.gz
,sub-barcelona01_T2w.nii.gz
,sub-brnoUhb03_T2w.nii.gz
) were moved from the training dataset to the test dataset. The reason is that I want to apply model M4 on these five images and compare the M4 predictions with manual segmentations from 4 raters -- the images cannot be in the training set (the model would be biased).sub-mgh01_T2w.nii.gz
,sub-mgh02_T2w.nii.gz
,sub-stanford02_T2w.nii.gz
,sub-stanford05_T2w.nii.gz
,sub-ucdavis03_T2w.nii.gz
), the images were QCed, manually corrected and added to the training dataset.Model M4