MIRO (Multimodal Integration through Relational Optimization) is a geometric deep learning framework that enhances clustering algorithms by transforming complex point clouds into an optimized structure amenable to conventional clustering methods.
MIRO employs recurrent graph neural networks (rGNNs) to learn a transformation that squeezes localization belonging to the same cluster toward a common center, resulting in a compact representation of clusters within the point cloud.
- Improved Clustering Performance: MIRO increases the efficiency of existing clustering algorithms by transforming point clouds into an optimized format.
- Simplified Parameter Selection: By enhancing the differentiation among clusters and their separation from the background, MIRO streamlines parameter selection for clustering methods like DBSCAN.
- Single-Shot and Few-Shot Learning: MIRO’s single- or few-shot learning capability allows it to generalize across scenarios with minimal training, making it highly efficient and versatile.
- Multiscale Clustering: MIRO’s recurrent structure allows for identifying patterns at different scales.
- Broad Applicability: MIRO is effective across datasets with diverse cluster shapes and symmetries.
Ready to dive into MIRO? Getting started is easy.
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Make sure you have Python 3.9 or higher installed.
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Clone the repository to your local machine:
git clone https://github.com/DeepTrackAI/MIRO.git
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Install the necessary dependencies:
pip install -r requirements.txt
Then unleash MIRO's full potential.
If you use MIRO in your research, please cite our paper:
@article{pineda2024spatial,
title={Spatial Clustering of Molecular Localizations with Graph Neural Networks},
author={Pineda, Jes{\'u}s and Mas{\'o}-Orriols, Sergi and Bertran, Joan and Goks{\"o}r, Mattias and Volpe, Giovanni and Manzo, Carlo},
journal={arXiv preprint arXiv:2412.00173},
year={2024}
}