| --- |
| 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} |
| } |
| ``` |
