Comments (1)
jcohenadad
commented on June 18, 2024
That works pretty well:
ScaleIntensityRangePercentilesd(keys=["image"], lower=10, upper=90, b_min=0.0, b_max=1.0, clip=True, relative=True),
Except in some cases where the region of interest is within the 10% extreme (with/without histnorm):
from model_seg_mouse-sc_wm-gm_t1.
Related Issues (20)
- Not enough WM/GM contrast
- Improper Dice calculation when using 2 classes HOT 1
- Hyperparameter optimization and wandb sweep
- Use post-pred transforms with Compose()
- Make sure image orientation is correct for inference
- Add citation for this repos HOT 2
- Consider using ensemble/bagging for higher performance on prediction HOT 2
- Access to wanb refused HOT 3
- Try smoothing (esp. along z) to gain performance at test-time HOT 1
- Try 2D nnUnet HOT 19
- Use active learning based on uncertainty of ensemble predictions
- Output uncertainty with ensemble/bagging
- Compare 2D nnU-Net results with UNet results HOT 10
- Train 3D nnU-Net on segmentation results of 2D nnU-Net HOT 2
- Final nnU-Net model training for zurich-mouse HOT 7
- Find out where models are typically stored HOT 4
- Create simple test.py inference script HOT 2
- Improvement of the model - smoothing after 2D nnU-Net inference
- Problem running inference on new data HOT 1
- Metrics measurement based on model output
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from model_seg_mouse-sc_wm-gm_t1.