brain-mri-segmentation

Production-grade binary semantic segmentation of brain tumors from MRI scans — pixel-level mask prediction for low-grade glioma (LGG) regions.

Overview

Task	Binary semantic segmentation (tumor vs. background)
Dataset	Mateusz Buda LGG MRI (TCGA) — 110 patients, 3 929 paired slices
Main model	SegFormer-B2 (nvidia/segformer-b2-finetuned-ade-512-512, ~25 M params)
Baseline	Small U-Net (4 levels, 32→256 ch, ~1.9 M params, hand-rolled)
Stack	PyTorch Lightning · Hydra · MLflow · DVC · FastAPI · Docker · GitHub Actions · MkDocs
License	MIT

Sections

• Architecture — data flow, model choices, metrics rationale
• Training — running experiments, logging, overrides
• Serving — FastAPI endpoints, Docker deployment
• Benchmarks — vs literature, trade-offs
• Reproducibility — pinned environment, one-command re-run
• Limitations — failure modes, dataset bias
• Model card — HF Hub card template

Links

• Code: GitHub
• Model: Hugging Face

Disclaimer

Research/educational artifact only — not intended for clinical use.