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README.md

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+---
+license: mit
+tags:
+  - semantic-segmentation
+  - medical-imaging
+  - brain-mri
+  - pytorch-lightning
+  - segformer
+library_name: pytorch
+datasets:
+  - mateuszbuda/lgg-mri-segmentation
+pipeline_tag: image-segmentation
+---
+
+# brain-mri-segmentation
+
+Production-grade binary brain-tumor MRI segmentation (LGG / TCGA).
+
+Binary semantic segmentation of brain-tumor regions (low-grade glioma) from FLAIR MRI slices. Main model: SegFormer-B2 fine-tuned on the Mateusz Buda LGG MRI dataset (TCGA, 110 patients, 3 929 paired slices) with a patient-level 80/10/10 split. Baseline: hand-rolled U-Net (4 levels, 32→256 ch, ~1.9 M params).
+
+Output is a binary mask at 256 × 256 resolution (1 = tumor, 0 = background).
+
+## Metrics
+
+| Metric | Value |
+|---|---|
+| Main model | SegFormer-B2 |
+| Main Dice | 65.5% |
+| Main IoU | 66.2% |
+| Main Pixel accuracy | 99.73% |
+| Baseline model | U-Net (small) |
+| Baseline Dice | 51.9% |
+| Baseline IoU | 57.7% |
+| Baseline Pixel accuracy | 99.66% |
+| Test size (slices) | 387 |
+
+## Usage
+
+```python
+from huggingface_hub import snapshot_download
+
+from brain_mri_segmentation.inference.predict import load_model, predict
+
+ckpt_dir = snapshot_download("kiselyovd/brain-mri-segmentation")
+model = load_model(f"{ckpt_dir}/best.ckpt")
+result = predict(model, "path/to/slice.png")
+# {"mask": [[0, 0, 1, ...], ...], "shape": [256, 256]}
+```
+
+## Intended use
+
+Research and educational purposes only. **This is not a medical device.** Do not use for clinical decision-making. The model was trained on a single publicly available dataset and has not been validated against clinical ground truth, population diversity, acquisition-device variation, or downstream clinical workflows.
+
+## Source
+
+https://github.com/kiselyovd/brain-mri-segmentation

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