theodpzz/ct-ssg

Structured Spectral Graph Representation Learning for Multi-label Abnormality Analysis from 3D CT Scans 🩺👨🏻‍⚕️

✅ PyTorch pretrained model weights "Structured Spectral Graph Representation Learning for Multi-label Abnormality Analysis from 3D CT Scans".

📄 Under review, preprint: arXiv preprint.

⚡️ PyTorch implementation available at https://github.com/theodpzz/ct-ssg.

🔥 Available resources

model_state_dict.pt: Model weights for CT-SSG trained on the CT-RATE training set.

thresholds.json: Per-abnormality classification thresholds optimized on our internal CT-RATE validation set. The official CT-RATE test set was not used during threshold optimization to preserve unbiased evaluation.

⚠️ Splits

Since the CT-RATE dataset does not provide an official train/validation/test split, we adopt the following protocol for training and evaluation of the released model.

The test set remains strictly untouched and corresponds to the official validation partition of CT-RATE, i.e., all patients whose identifiers contain the tag 'valid'.

For internal validation, we use a subset of the original training data consisting of patients with IDs ranging from 1 to 1308 (denoted as train_1 to train_1308).

The released model is trained on the remaining portion of the CT-RATE training set, excluding the aforementioned validation subset. In other words, all training samples outside the ID range 1-1308 are used for model training.

🤝🏻 Acknowledgment

We thank contributors from the CT-RATE dataset available at https://huggingface.co/datasets/ibrahimhamamci/CT-RATE, from the Rad-ChestCT dataset available at https://zenodo.org/records/6406114 and from the Merlin Abdominal CT dataset available at https://stanfordaimi.azurewebsites.net/categories/datasets?domain=BODY.

Purpose

The model, trained on a publicly available dataset, is provided for academic and research purposes only, to support reproducibility of the results described in the associated paper. This repository is a research prototype, and is not intended for clinical use.

📎Citation

If you find this repository useful for your work, we would appreciate the following citation:

@article{dipiazza_2026_ctssg,
        title = {Structured Spectral Graph Representation Learning for Multi-label Abnormality Analysis from 3D CT Scan},
        author = {Di Piazza, Theo and Lazarus, Carole and Nempont, Olivier and Boussel, Loic},
        year = {2026},
        note = {Preprint, under review},
}