--- 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 πŸ©ΊπŸ‘¨πŸ»β€βš•οΈ

βœ… PyTorch pretrained model weights of"Imitating Radiological Scrolling: A Global-Local Attention Model for 3D Chest CT Volumes Multi-Label Anomaly Classification". πŸ“„ 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).

## πŸ”₯ 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). ## πŸ“ŽCitation 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}, } ```