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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}
}
``` |