---
license_link: https://github.com/adarshbhandaryp/ABMIL
datasets:
- ODELIA-AI/ODELIA-Challenge-2025
language:
- en
metrics:
- roc_auc
pipeline_tag: image-classification
tags:
- breast
- cancer
- mri
- odelia
- abmil
- swin-transformer
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 ABMIL 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 ABMIL Model

Attention-Based Multiple Instance Learning (ABMIL) model for subject-level
three-class breast MRI classification in the ODELIA Challenge 2025.

This model was contributed by the ABMIL team. The source implementation is
available at [adarshbhandaryp/ABMIL](https://github.com/adarshbhandaryp/ABMIL).

## Method

The model represents each subject using 32 axial slices with pre-contrast,
early post-contrast, and subtraction channels. A Swin Transformer Tiny
backbone extracts slice features. Gated attention aggregates the slice
features into a patient-level representation, followed by a three-class
classifier. The source README reports focal loss with gamma 5.0.

## 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

The source repository provides the complete training and evaluation code and
requirements:

```bash
git clone https://github.com/adarshbhandaryp/ABMIL.git
cd ABMIL
pip install -r requirements.txt
```

Refer to the source repository for configuration-file usage and preprocessing
requirements. The model expects ODELIA-style multi-sequence breast MRI input.

## License and attribution

This repository is published under the ODELIA and ABMIL authors' permission.

The Swin Transformer implementation and pretrained ImageNet weights are
third-party components. Their applicable licenses and terms must be preserved
and reviewed before redistribution. See the dependency notices in the source
repository and the relevant upstream package/model licenses.

Please cite or acknowledge:

- ODELIA Challenge 2025 and the ODELIA consortium.
- The ABMIL authors and [source repository](https://github.com/adarshbhandaryp/ABMIL).
- The ODELIA paper: [arXiv:2506.00474](https://arxiv.org/abs/2506.00474).