README.md

---
license: cc-by-nc-sa-4.0
datasets:
- ibrahimhamamci/CT-RATE
tags:
- chest-ct
- radiology
- 3d-medical-imaging
- medical
- ct-rate
- abnormality-classification
- computer-vision
---

<p align="center">
  <h2 align="center">[MIDL 2025] Imitating Radiological Scrolling: A Global-Local Attention Model for 3D Chest CT Volumes Multi-Label Anomaly Classification 🩺👨🏻‍⚕️</h2>
</p>

✅ PyTorch pretrained model weights of"Imitating Radiological Scrolling: A Global-Local Attention Model for 3D Chest CT Volumes Multi-Label Anomaly Classification".

📄 Accepted at MIDL 2025: [arXiv preprint](https://arxiv.org/abs/2503.20652).

⚡️ PyTorch implementation available at [https://github.com/theodpzz/ct-scroll](https://github.com/theodpzz/ct-scroll).

<p align="center">
  <img src="https://huggingface.co/theodpzz/ct-scroll/resolve/main/figures/method_overview.png" width="100%">
</p>

## 🔥 Available resources

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

**./ckpt/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.

## 🤝🏻 Acknowledgment

We thank contributors from the CT-RATE dataset available at [https://huggingface.co/datasets/ibrahimhamamci/CT-RATE](https://huggingface.co/datasets/ibrahimhamamci/CT-RATE), and from the Rad-ChestCT dataset available at [https://zenodo.org/records/6406114](https://zenodo.org/records/6406114).

## 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:

```bibtex
@InProceedings{dipiazza_2025_ctscroll,
        title = {Imitating Radiological Scrolling: A Global-Local Attention Model for 3D Chest CT Volumes Multi-Label Anomaly Classification},
        author = {Di Piazza, Theo and Lazarus, Carole and Nempont, Olivier and Boussel, Loic},
        booktitle = {Proceedings of The 8nd International Conference on Medical Imaging with Deep Learning -- MIDL 2025},
        year = {2025},
        publisher = {PMLR},
}
```