| title: RADAR Abdominal CT | |
| emoji: 🩻 | |
| colorFrom: blue | |
| colorTo: green | |
| sdk: gradio | |
| app_file: app.py | |
| pinned: false | |
| license: cc-by-nc-sa-4.0 | |
| python_version: "3.12" | |
| sdk_version: "5.49.1" | |
| preload_from_hub: | |
| - radar-generalist/RADAR checkpoint_radar_pretrain.pth,bert-base-chinese/config.json,bert-base-chinese/config_decoder.json,bert-base-chinese/pytorch_model.bin,bert-base-chinese/tokenizer.json,bert-base-chinese/tokenizer_config.json,bert-base-chinese/vocab.txt | |
| RADAR abdominal-CT findings demo: upload a contrast-enhanced abdominal CT as a single volume (`.nii` / `.nii.gz`, `.nrrd`, `.mha`) or a DICOM series (extensionless or `.dcm` files, or a `.zip`, nested folders OK); get top-finding scores plus a table of all 146 per-finding scores. | |
| - Paper/model: [radar-generalist/RADAR](https://huggingface.co/radar-generalist/RADAR) (CC BY-NC-SA 4.0, non-commercial research use only) | |
| - Source: vendored inference code from the RADAR release (Zenodo `damo-radar.zip`, record `21271172`); weights load at runtime from the public HuggingFace repo, no token needed | |
| - Intake: everything normalizes to one `.nii.gz` via SimpleITK (no new deps); header+raw pairs (`.mhd`/`.nhdr`) need converting first. Multi-file uploads are treated as one DICOM series (largest series wins, ≥8 slices); a lone `.dcm` is rejected as one slice, not a volume | |
| - Runtime: ZeroGPU (`large`); CPU staging/conversion runs quota-free, then one `@spaces.GPU(duration=90)` scoring call per case. Preprocessing (1x1x5mm resample, 96x256x384 pad/crop) is the upstream MONAI chain, reused verbatim (`transformers==4.48.3`; v5's loader crashes on this 2022-vintage BERT fork) | |
| Research assistance tool, not a medical diagnosis. | |