metadata
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 headt5/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