license: cc-by-nc-4.0
datasets:
- ibrahimhamamci/CT-RATE
[MIDL 2025] Imitating Radiological Scrolling: A Global-Local Attention Model for 3D Chest CT Volumes Multi-Label Anomaly Classification 🩺👨🏻⚕️
✅ Official implementation of the paper "Imitating Radiological Scrolling: A Global-Local Attention Model for 3D Chest CT Volumes Multi-Label Anomaly Classification".
📄 Paper accepted for publication at MIDL 2025: arXiv preprint.
⚡️ Source code available at https://github.com/theodpzz/ct-scroll.
🔥 Available resources
ckpt/model_state_dict.pt: Model trained on the CT-RATE train set.
ckpt/classification_threshold.csv: Classification thresholds optimized on our validation set, leaving the official CT-RATE test set untouched.
🤝🏻 Acknowledgment
We thank contributors from the CT-RATE dataset available at https://huggingface.co/datasets/ibrahimhamamci/CT-RATE, and from the Rad-ChestCT dataset available at https://zenodo.org/records/6406114.
📎Citation
If you use this repository in your work, we would appreciate the following citation:
@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},
}