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README.md

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+---
+license: mit
+tags:
+  - brain-tumor-segmentation
+  - radiology-report-generation
+  - brats2020
+  - medical-imaging
+  - text-guided-segmentation
+---
+
+# TextBraTS-arch3: Lightweight 2.5D Text-Guided Brain Tumor Segmentation
+
+A lightweight 2.5D convolutional network for brain tumor segmentation + radiology report generation on the TextBraTS/BraTS2020 dataset.
+
+## Results (93-patient official test set)
+
+| Metric | Value |
+|---|---|
+| Avg Dice | 83.8% |
+| Avg HD95 | 3.41 mm |
+| ET Dice | 79.7 |
+| WT Dice | 89.5 |
+| TC Dice | 82.3 |
+
+Beats TextCSP SOTA (4.81mm) and TextBraTS (5.13mm) on HD95 at **10.4M parameters**.
+
+## Repo structure
+
+- `segmentation/best_avg.pt` — segmentation model checkpoint (epoch 64, val-selected)
+- `t5/model.safetensors` + tokenizer — fine-tuned T5-small report generation head
+- `t5/img_proj.pt` — image-conditioned projection weights for T5
+
+## Architecture
+
+- **Input:** 2.5D — 4 MRI modalities × 3 adjacent axial slices = 12 channels at 128×128
+- **Encoder:** 4-stage ResNet (32→64→128→256 ch)
+- **Text:** Offline RadBERT embeddings (zero forward-pass text cost)
+- **Decoder:** Attention-gated skips + soft cascade WT→TC→ET
+- **Report gen:** ImageConditionedT5-small (8 image-prefix soft tokens)
+
+## Dataset
+
+TextBraTS / BraTS2020 — official split: 220 train / 56 val / 93 test