Brain MRI Tumor Segmentation
Investigated multiple 3D medical image segmentation architectures for brain tumor delineation using the BraTS dataset, evaluating baseline and attention-enhanced models.
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SYSTEM ARCHITECTURE
Best validation Dice: 0.8256 using the Attention + EfficientNet architecture.
Problem
Brain tumor segmentation requires accurate volumetric understanding across noisy medical scans, with strong constraints on precision and clinical interpretability.
Architecture
3D segmentation pipeline evaluating baseline and attention-enhanced encoder-decoder architectures on BraTS data, with patch-based training and mixed-precision optimization.
Challenges
- Handled volumetric 3D MRI inputs with patch-based training
- Compared baseline 3D U-Net and attention-enhanced variants
- Balanced segmentation quality with memory limits on large 3D tensors
- Tuned loss functions for class imbalance in tumor regions
- Optimized GPU memory with mixed precision and gradient checkpointing
Benchmarks
BraTS dataset, 128×128×128 patch size. Validation Dice progression: baseline 3D U-Net 0.8057 → EfficientNet encoder 0.8157 → Attention U-Net + EfficientNet 0.8256 (+1.99 pp improvement over baseline). Mixed precision training with MONAI.
Lessons Learned
- Attention mechanisms can improve localization in medical volumes
- 3D models are memory-bound quickly, so training strategy matters
- Dice-based objectives are essential for imbalanced segmentation tasks
- Incremental experimentation beats architectural complexity