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README.md

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+---
+license: mit
+library_name: pytorch
+pipeline_tag: image-classification
+tags:
+  - medical-imaging
+  - breast-ultrasound
+  - bi-rads
+  - retrieval-augmented-generation
+  - vision-language
+---
+
+# B-RAD inference checkpoints
+
+Released inference weights for **B-RAD**, an interpretable multimodal
+retrieval-augmented diagnosis pipeline for breast ultrasound. B-RAD turns a
+single breast ultrasound image into an interpretable BI-RADS assessment by
+chaining four modules: BI-RADS-aware retrieval, retrieval-guided few-shot
+detection, foundational segmentation, and region-aware classification.
+
+- **Paper**: [npj Digital Medicine (2026)](https://www.nature.com/articles/s41746-026-03272-3)
+- **DOI**: [10.1038/s41746-026-03272-3](https://doi.org/10.1038/s41746-026-03272-3)
+- **Code**: [github.com/snuh-rad-aicon/B-RAD](https://github.com/snuh-rad-aicon/B-RAD)
+
+## Files
+
+| File | Size | Module | Contents |
+|---|---:|---|---|
+| `b_rad_clip.pt` | 1.98 GiB | B-RAD-CLIP | VPT-adapted BiomedCLIP image tower, BioLinkBERT-large text tower, both 512-d projection heads, the BI-RADS ordinal heads, and the learned temperature |
+| `b_rad_full.pt` | 1.98 GiB | B-RAD-FULL | the same encoder plus the 5-fold BI-RADS classification ensemble (`mlp_heads`) and its per-fold standard scalers |
+| `b_rad_det.pt` | 339 MiB  | B-RAD-Det | detector state dict: ViT-B/16 backbone, OWL-ViT-style detection head, and the four learned class queries |
+
+SHA-256:
+
+```text
+bb0e2827dd2c1e14741a567cbd960d8579b0d3c07fc544c6f844c9246b61fe30  b_rad_clip.pt
+0989a3e8b1f640a1b5c8679056cec2494b0ee07a7d48b0ab8b5e4e37baadef3e  b_rad_full.pt
+1a6a86433d7fc381c20a380a42217d1bf1ba4e9558a3becabe28b4ee9656e4de  b_rad_det.pt
+```
+
+`b_rad_det.pt` is the fold-0 stage-2 detector.
+
+## Usage
+
+```bash
+git clone https://github.com/snuh-rad-aicon/B-RAD && cd B-RAD
+conda env create -f environment.yml && conda activate b-rad
+
+bash B-RAD/ckpts/download_weights.sh    # fetches this repo, plus UltraSam.pth
+bash B-RAD/scripts/inference_full.sh    # end-to-end on the bundled sample
+```
+
+`notebooks/brad_inference_demo.ipynb` runs the same pipeline with a
+visualization of every stage. The segmentation module needs its own OpenMMLab
+environment; see the repository README.
+
+`UltraSam.pth` for B-RAD-Seg is not hosted here; `download_weights.sh` fetches it
+from the [UltraSam](https://github.com/CAMMA-public/UltraSam) authors.
+
+## Where each file is used
+
+```text
+B-RAD/
+├── B-RAD-CLIP/     infer_retrieval.py   --ckpt  b_rad_clip.pt
+├── B-RAD-Det/      inference_single.py  --ckpt  b_rad_det.pt
+├── B-RAD-Seg/      infer_bbox.py        --ckpt  UltraSam.pth
+├── B-RAD-FULL/     infer_birads.py      --ckpt  b_rad_full.pt
+└── ckpts/          download_weights.sh  (puts all four here)
+```
+
+## Intended use and limitations
+
+Research use only. These weights are not a medical device and must not be used
+for clinical decision making. They were trained on public breast-ultrasound
+cohorts plus a single-institution clinical cohort, so performance on other
+scanners, populations, or acquisition protocols is not characterized.
+
+## Citation
+
+```bibtex
+@article{jang2026interpretable,
+  title     = {Interpretable multimodal retrieval augmented diagnosis for breast ultrasound with multinational clinical validation and reader study},
+  author    = {Jang, Han and An, Subin and Jung, Sunghyun and Ji, Hye and Kim, Miran and Lee, Soochahn and Choi, Kyu Sung and Ha, Su Min},
+  journal   = {npj Digital Medicine},
+  year      = {2026},
+  publisher = {Nature Publishing Group UK London},
+  doi       = {10.1038/s41746-026-03272-3},
+  url       = {https://doi.org/10.1038/s41746-026-03272-3}
+}
+```
+
+## License
+
+MIT, matching the [code repository](https://github.com/snuh-rad-aicon/B-RAD/blob/main/LICENSE).
+Upstream components retain their original licenses: BiomedCLIP, BioLinkBERT,
+OWL-ViT, and the `timm` ViT-B/16 initialization. `UltraSam`, distributed
+separately, is CC BY-NC-SA 4.0 and non-commercial only.