| --- |
| datasets: |
| - ODELIA-AI/ODELIA-Challenge-2025 |
| language: |
| - en |
| metrics: |
| - roc_auc |
| pipeline_tag: image-classification |
| tags: |
| - breast |
| - cancer |
| - mri |
| - odelia |
| - resnet |
| - 3d-classification |
| extra_gated_prompt: >- |
| ### Model Usage Agreement |
| |
| By accessing or using this model (the "Model"), you acknowledge and agree |
| to the following terms: |
|
|
| 1. The Model is provided for non-commercial academic and research use only. |
| 2. The Model must not be used for diagnosis, treatment, clinical decision |
| making, or any application involving real-patient care. |
| 3. Do not identify, re-identify, or deanonymize people whose data may have |
| contributed to the training or evaluation data. |
| 4. Publications or derivative research must acknowledge the ODELIA |
| consortium and cite the Pimed authors and source repository. |
| 5. You are responsible for validating outputs in your research context. |
| The Model is provided as-is, without warranties. |
|
|
| This gated access does not grant rights beyond the license and permissions |
| stated in this model card. Third-party components retain their own licenses. |
| --- |
| |
| # ODELIA Pimed Model |
|
|
| Three-class 3D breast MRI classification model contributed by the Pimed team |
| to the ODELIA Challenge 2025. |
|
|
| The source repository is |
| [dnschouten/odelia_submission_pimed](https://github.com/dnschouten/odelia_submission_pimed). |
|
|
| ## Method |
|
|
| The corresponding implementation in the ODELIA project uses a 3D ResNet-18 |
| backbone with one input channel and three output classes. It wraps the model |
| for subject-level classification of ODELIA MRI volumes. |
|
|
| The exact preprocessing, checkpoint configuration, and evaluation procedure |
| must be taken from the authors' source repository and the files uploaded with |
| this model. Do not infer clinical performance from the architecture alone. |
|
|
| ## Intended use and limitations |
|
|
| This release is for research and benchmarking only. It is not a medical |
| device, is not clinically validated, and must not be used for diagnosis, |
| prognosis, treatment, triage, or other clinical decisions. Performance may |
| vary across institutions, scanners, acquisition protocols, and populations. |
|
|
| ## Setup |
|
|
| Use the source repository for the authoritative setup and inference commands: |
|
|
| ```bash |
| git clone https://github.com/dnschouten/odelia_submission_pimed.git |
| cd odelia_submission_pimed |
| ``` |
|
|
| ## License and attribution |
|
|
| The implementation uses PyTorch/MONAI components. Their applicable licenses, |
| notices, and any pretrained-weight terms must be preserved and reviewed before |
| redistribution. |
|
|
| Please cite or acknowledge: |
|
|
| - ODELIA Challenge 2025 and the ODELIA consortium. |
| - The Pimed authors: Daan Schouten, Jeong Hoon Lee, Mirabela Rusu and [source repository](https://github.com/dnschouten/odelia_submission_pimed). |
| - The ODELIA paper: [arXiv:2506.00474](https://arxiv.org/abs/2506.00474). |
|
|
| For questions regarding model architecture, training, or permissions, please contact **Daan Schouten**. |
