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v1.0.0 — Brain Tumor Segmentation (Val Dice 0.7894)

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@motazalqaoud motazalqaoud released this 28 Jun 03:51
· 6 commits to main since this release
v1.0.0
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Learn about vigilant mode.

First release of Brain Tumor Segmentation — 3D Attention U-Net

Trained for 50 epochs on the Kaggle Brain Tumor 12K dataset using a CPU-optimised pseudo-3D pipeline.

Results

Metric Value
Val Dice 0.7894
Train Dice 0.9000
Overfitting gap 0.11 ✓
Model parameters 559K
Training time ~6 hours (CPU)

Checkpoint

best_model_dice_0.7894.pt (6.6 MB) is attached — download and run inference without retraining:

python scripts/predict3d.py \
  --checkpoint best_model_dice_0.7894.pt \
  --image path/to/brain_mri.jpg \
  --out prediction.png

What's included

  • 3D Attention U-Net with SE channel attention and spatial attention gates
  • Hybrid loss (Weighted Dice + Focal + Boundary)
  • Hardware presets: CPU / 8GB GPU / 16GB GPU
  • run.py automation for non-technical users
  • Full inference pipeline with confidence maps

See the full README and Evaluation Results wiki page for details.

Assets 3
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