---
license: apache-2.0
tags:
  - brain-tumor-segmentation
  - missing-modality
  - medical-imaging
  - adaptive-gating
  - BraTS
datasets:
  - BraTS2020
pipeline_tag: image-segmentation
---

# Text-Guided Decision Support System

Modality-Aware Adaptive Fusion for Brain Tumor Segmentation under Missing MRI Modalities.

## Model Description

A 2.5D U-Net with cross-attention text fusion and adaptive gate that 
dynamically adjusts text contribution based on modality availability. 
Trained on BraTS 2020 (369 patients) with systematic modality dropout.

- **Parameters:** 21.4M
- **Input:** 4 MRI modalities (FLAIR, T1CE, T2, T1) — any subset supported
- **Output:** 3-region segmentation (ET, NCR, ED) + clinical report
- **Text encoder:** Frozen BioBERT (768-dim)

## Links

- **Code & Demo:** [GitHub](https://github.com/HeeKuk99/Text_guided_decision_support_system)
- **Paper:** on going
  
## Performance (BraTS 2020, 74-patient test set)

| Metric | Score |
|--------|-------|
| 15-scenario avg Dice | 0.7644 |
| T1CE-missing avg Dice | 0.8036 |
| vs RFNet (T1CE-missing) | +0.097 |

## Usage

```python
# See https://github.com/HeeKuk99/Text_guided_decision_support_system
python app.py  # launches Gradio demo at localhost:7860
```

## Citation

```bibtex
@article{textguided2026,
  title={Modality-Aware Adaptive Text-Visual Fusion for Robust Brain Tumor 
         Segmentation with Missing MRI Modalities},
  year={2026}
}
```