You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

Log in or Sign Up to review the conditions and access this model content.

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

✅ CT-SSG model weights, trained on CT-RATE.

📄 Accepted at MELBA 2026: https://arxiv.org/abs/2510.10779.

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

👁 Image

🔥 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_ssg_2026,
 title = "Structured Spectral Graph Representation Learning for Multi-label Abnormality Analysis from 3D CT Scans",
 author = "Di Piazza, Theo and Lazarus, Carole and Nempont, Olivier and Boussel, Loic",
 journal = "Machine Learning for Biomedical Imaging",
 volume = "2026",
 issue = "June 2026 issue",
 year = "2026",
 pages = "359--388",
 issn = "2766-905X",
 doi = "https://doi.org/10.59275/j.melba.2026-87e3",
 url = "https://melba-journal.org/"
}