README.md

metadata

title: Jolia
emoji: 🫁
colorFrom: red
colorTo: gray
sdk: gradio
sdk_version: 6.22.0
app_file: app.py
short_description: Zero-shot CT findings with the Jolia 3D CT foundation model
python_version: '3.12'
startup_duration_timeout: 1h
pinned: false
license: other

Jolia — zero-shot CT analysis

Demo of raidium/Jolia, a 3D CT foundation model that encodes a whole chest / abdominal CT volume into a global embedding and 102 named organ-query embeddings, both aligned with radiology-report text.

Upload a NIfTI CT volume and:

• score free-text findings against the whole volume (global CLIP head), and
• route short findings phrases to a single organ query (ParallelOrganCLIP head, each organ with its own trained temperature and bias).

The pipeline follows example_zero_shot.py from the model repo exactly: JoliaPreprocessor (1.5 mm isotropic, 192³ centre crop, 11 CT windowing channels) for the image, and the paired Qwen/Qwen3-Embedding-8B text encoder (last-token pooling, context length 512) for the prompts.

⚠️ Research preview. Not a medical device; not for clinical use.

Example volumes

The bundled example CTs come from the TotalSegmentator dataset (Wasserthal et al., Zenodo record 10047292, CC-BY-4.0), downloaded via YongchengYAO/TotalSegmentator-CT-Lite. File names carry that dataset's own study-type / pathology metadata. Attribution:

Wasserthal, J. et al. TotalSegmentator: Robust segmentation of 104 anatomic structures in CT images. Radiology: Artificial Intelligence (2023). Dataset licensed CC-BY-4.0.

Notes

• Volumes are reoriented to the radiological axial layout (rows anterior→posterior, columns right→left, slices inferior→superior) before JoliaPreprocessor, which then flips depth and centre-crops.
• Probabilities are sigmoid(calibrated logit) — a per-pair "is this a match?" score, not a softmax over prompts.
• DICOM series can be converted to NIfTI with dcm2niix.