OrganLens

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

File	Size	Purpose
teacher_checkpoint.pth	1.3 GB	Organ-conditioned backbone and mask decoder for embedding extraction
heads/organlens_ctrate_heads_524999.pt	397 MB	Bundled MLP heads for 11 organ views and 18 CT-RATE diseases
heads/organlens_radchest_heads_524999.pt	507 MB	Bundled MLP heads for 11 organ views and 23 RAD-ChestCT diseases

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.