OrganLens

OrganLens

Paper | Official implementation

This is the official model release for OrganLens: Organ-Specific Representation Learning for CT Foundation Models.

OrganLens learns organ-conditioned representations from chest CT volumes. The released model accepts a NIfTI CT volume and extracts a 1024-dimensional embedding for any subset of 11 anatomical organs using the paper's all-slice, soft-mask-area-weighted pooling method.

The implementation, preprocessing pipeline, training scripts, and evaluation commands are available in the OrganLens GitHub repository.

Released files

The head bundles do not contain the teacher weights. Raw-volume prediction requires the teacher checkpoint and the bundle for the target dataset. Embedding extraction requires only the teacher checkpoint.

These files are distinct from GigaHeart's pytorch_model.bin. That checkpoint initializes new OrganLens backbone training; teacher_checkpoint.pth is the resulting OrganLens model used for inference.

Download

Install the Hugging Face command-line client and download the complete release:

pip install -U huggingface_hub
hf download gezx1004/OrganLens --local-dir ./organlens_checkpoints

To download only the teacher:

hf download gezx1004/OrganLens teacher_checkpoint.pth \
  --local-dir ./organlens_checkpoints

Installation

git clone https://github.com/gezhixuan/OrganLens.git
cd OrganLens
pip install .

Extract organ embeddings

Extract embeddings for all 11 organs:

organlens-infer \
  --dataset ctrate \
  --volume /path/to/volume.nii.gz \
  --teacher-checkpoint ./organlens_checkpoints/teacher_checkpoint.pth \
  --embedding-output ./case_001.pt

Extract a selected subset:

organlens-infer \
  --dataset ctrate \
  --volume /path/to/volume.nii.gz \
  --teacher-checkpoint ./organlens_checkpoints/teacher_checkpoint.pth \
  --organs heart lung aorta \
  --embedding-output ./case_001.pt

Supported organ names are:

spleen kidneys liver stomach pancreas lung esophagus trachea intestine heart aorta

Direct CT-RATE prediction

organlens-infer \
  --dataset ctrate \
  --volume /path/to/volume.nii.gz \
  --teacher-checkpoint ./organlens_checkpoints/teacher_checkpoint.pth \
  --head-bundle ./organlens_checkpoints/heads/organlens_ctrate_heads_524999.pt \
  --embedding-output ./case_001.pt \
  --prediction-output ./case_001_predictions.json

See the GitHub README for preprocessing, training, RAD-ChestCT evaluation, the Python API, and multi-GPU benchmarking. For CT-RATE, preprocessing follows the convention of the official CT-CLIP repository while determining orientation from the NIfTI header.

Intended use and limitations

OrganLens is released for research and reproducibility in chest CT representation learning. It is not a medical device, diagnostic system, or clinical decision-support tool, and it has not been validated for clinical deployment. Predictions require independent validation for any new population or acquisition protocol.

The code repository is licensed under Apache-2.0. The released weights are provided for research use and remain subject to applicable upstream model and training-dataset terms, including the GigaHeart usage notice and the CT-RATE terms. Users are responsible for reviewing those terms before use or redistribution.

Citation

@misc{ge2026organlensorganspecificrepresentationlearning,
  title         = {OrganLens: Organ-Specific Representation Learning for CT Foundation Models},
  author        = {Zhixuan Ge and Anqi Li and Sadeer Al-Kindi and Hanwen Xu and Wei Qiu},
  year          = {2026},
  eprint        = {2607.25164},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CV},
  url           = {https://arxiv.org/abs/2607.25164}
}