---
license: cc-by-nc-4.0
datasets:
- ibrahimhamamci/CT-RATE
tags:
- chest-ct
- radiology
- 3d-medical-imaging
- medical
- ct-rate
- abnormality-classification
- computer-vision
---
[MIDL 2025] Imitating Radiological Scrolling: A Global-Local Attention Model for 3D Chest CT Volumes Multi-Label Anomaly Classification π©Ίπ¨π»ββοΈ
β
PyTorch pretrained model weights of"Imitating Radiological Scrolling: A Global-Local Attention Model for 3D Chest CT Volumes Multi-Label Anomaly Classification".
π Accepted at MIDL 2025: [arXiv preprint](https://arxiv.org/abs/2503.20652).
β‘οΈ PyTorch implementation available at [https://github.com/theodpzz/ct-scroll](https://github.com/theodpzz/ct-scroll).
## π₯ 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.
## π€π» 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).
## πCitation
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},
}
```