a global embedding (embed_dim = 576), and
per-organ embeddings — 102 named organ slots produced by organ-query cross-attention pooling, trained to align with per-organ report text.

Installation

pip install torch transformers timm einops numpy safetensors

Quick start

python

import torch
from transformers import AutoModel

model = AutoModel.from_pretrained("raidium/Jolia", trust_remote_code=True).eval()

# image: a preprocessed CT volume, shape (B, 11, 192, 192, 192) — see Preprocessing
with torch.no_grad():
    cls = model(image).pooler_output          # (B, 576) global embedding

Preprocessing

Raw CT volumes must be brought to the Atlas input format ((11, 192, 192, 192): 1.5 mm isotropic, 192³ crop, 11 CT windowing channels). Grab the bundled preprocessor from the repo:

python

from huggingface_hub import snapshot_download
import sys
repo = snapshot_download("raidium/Jolia")
sys.path.append(repo)
from preprocessing_jolia import JoliaPreprocessor

pre = JoliaPreprocessor()
# volume: (H, W, D) in Hounsfield units; resolution in mm (row, col, slice)
image = pre(volume, resolution=(0.7, 0.7, 1.0)).unsqueeze(0)   # (1, 11, 192, 192, 192)

Working with organ queries (the easy way)

Per-organ embeddings are addressed by name

python

# All 102 organs as {name: (B, 576)}
organs = model.encode_organs(image)

# A subset, L2-normalized (cosine-ready)
sub = model.encode_organs(image, organs=["liver", "spleen", "pancreas"], normalize=True)

print(model.organ_slot_names)   # the 102 available organ names

For linear probing, the concatenated normalized feature is one call:

flat = model.extract_flat_feature(image) # (B, 576 * (1 + num_organs))

Zero-shot classification

Jolia ships with the CLIP text-projection head it was trained with. Pair it with the text encoder Jolia was trained against (Qwen/Qwen3-Embedding-8B) to classify a CT against arbitrary text prompts with no fine-tuning.

The text encoder is the heavy piece (~18 GB), so loading it is opt-in. Jolia bundles a small helper, JoliaTextEncoder, that handles tokenization and the (attention-mask-aware) last-token pooling the model was trained with.

python

import sys, torch
from huggingface_hub import snapshot_download
from transformers import AutoModel

# 1) Vision: Jolia from the Hub (self-contained, ~89 MB).
jolia = AutoModel.from_pretrained("raidium/Jolia", trust_remote_code=True).eval()

# 2) Text: Qwen3-Embedding-8B + Jolia's bundled JoliaTextEncoder helper.
repo = snapshot_download("raidium/Jolia"); sys.path.append(repo)
from text_encoder_jolia import JoliaTextEncoder
text_encoder = JoliaTextEncoder.from_pretrained(
    "Qwen/Qwen3-Embedding-8B",
    dtype=torch.bfloat16,        # ~18 GB at fp32; bf16 halves it
    device_map="auto",           # or .to("cuda")
).eval()

# 3) Zero-shot classification on a preprocessed CT volume.
prompts = ["a CT showing a liver lesion", "a CT showing pneumonia", "a normal abdominal CT"]
with torch.no_grad():
    text_features = text_encoder(prompts)                  # (N, 4096) last-token-pooled
    logits = jolia.zero_shot(image, text_features)         # (B, N) — calibrated CLIP logits
    probs  = torch.sigmoid(logits)                         # per-pair "is this a match?" probability

# Same output as `MultimodalCLSZeroShotCLIP.get_logits_per_image` in rarm.
# Pass `calibrated=False` if you want raw cosine in [-1, 1] (ranking-only):
cosine = jolia.zero_shot(image, text_features, calibrated=False)

Per-organ (query-routed) zero-shot

Jolia also ships the ParallelOrganCLIP text head it was trained against the per-organ findings of each report. This routes a text prompt to one specific organ's query embedding — useful when you want to ask "is there a lesion in the liver?" rather than scoring against the whole-volume CLS.

python

text_features = text_encoder(["a lesion", "looks normal"])    # (N, 4096)

# Score N prompts against a single organ — calibrated CLIP logits (B, N)
liver_logits = jolia.zero_shot_organ(image, text_features, organ="liver")
liver_probs  = torch.sigmoid(liver_logits)

# Score N prompts against many organs at once -> {organ_name: (B, N)}
scores = jolia.zero_shot_organs(
    image, text_features, organs=["liver", "spleen", "kidneys", "pancreas"]
)

# Raw cosine if you only need ranking and don't want the bias offset:
cosine = jolia.zero_shot_organ(image, text_features, organ="liver", calibrated=False)

Each organ has its own trained temperature and bias (the (200,)-shaped organ_logit_scale / organ_text_bias), automatically applied when calibrated=True. jolia.organ_slot_names lists the 102 organs that can be routed. The per-organ head uses a different text projection than the global one (encode_text vs encode_organ_text), trained on per-organ findings text.

A runnable, self-contained script is bundled as example_zero_shot.py.

Model details


Backbone	`MultiModalAtlas` — multi-scale 3D ViT, `dim=192`, heads `6`, stages `[2, 2, 8]`
Patch embed	`6×6×6`, 11 input channels (CT windowing), `merge_ratio = 4³`
Global embedding	576-d
Organ queries	102 slots × 192-d × 3 scales → 576-d
Parameters	~22 M (89 MB `safetensors`)
Input	`(B, 11, 192, 192, 192)` float32
Training data	INSPECT, CT-RATE, Stanford-Abdominal-CT (chest + abdomen CT)
Objectives	Volume–report CLIP + per-organ ParallelOrganCLIP
Paired text encoder	`Qwen/Qwen3-Embedding-8B` (last-token pooling, context length 512)
Global text projection	Linear `4096 → 576` (+ scalar temperature + bias) — global CLIP head
Per-organ text projection	Linear `4096 → 576` (+ per-organ temperature + bias, both `(200,)`) — ParallelOrganCLIP head

The 102 organ-slot names are the alphabetically-sorted union of per-organ report sections across the training datasets; slots 102–199 are unused padding. Methods like encode_organs expose only the named slots.

Outputs

model(image) returns a JoliaOutput with:

pooler_output — (B, 576) global embedding,
organ_queries — (B, num_organs, 576), populated when called with output_organ_queries=True.

Intended use & limitations

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

Jolia is a feature extractor for downstream radiology tasks (classification, retrieval, per-organ analysis) via linear probing or fine-tuning. It is trained on adult chest/abdominal CT and will not generalize to other modalities or unusual acquisition protocols. It does not produce diagnoses and must not be used for clinical decision-making.

Citation

bibtex

@misc{raidium_jolia,
  title  = {Jolia: a 3D CT Atlas foundation model with per-organ queries},
  author = {Raidium},
  year   = {2026},
  howpublished = {\url{https://huggingface.co/raidium/Jolia}}
}