---
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