Pulaski ProbUNet2D FID VSeg

🚀 PULASki_ProbUNet2D_FID_VSeg

In the field of medical imaging, this project offers a computationally efficient generative tool for biomedical image segmentation, addressing challenges like annotation variability and data scarcity.

🚀 Quick Start

In the domain of medical imaging, many supervised learning based methods for segmentation face several challenges such as high variability in annotations from multiple experts, paucity of labelled data and class imbalanced datasets. These issues may result in segmentations that lack the requisite precision for clinical analysis and can be misleadingly overconfident without associated uncertainty quantification.

We proposed the PULASki as a computationally efficient generative tool for biomedical image segmentation that accurately captures variability in expert annotations, even in small datasets. Our approach makes use of an improved loss function based on statistical distances in a conditional variational autoencoder structure (Probabilistic UNet), which improves learning of the conditional decoder compared to the standard cross-entropy particularly in class imbalanced problems.

✨ Features

This model can accurately capture the variability in expert annotations, even when dealing with small datasets. It uses an improved loss function in a conditional variational autoencoder structure, enhancing the learning of the conditional decoder, especially for class - imbalanced problems.

📚 Documentation

Model Details

It was introduced in PULASki: Learning inter-rater variability using statistical distances to improve probabilistic segmentation by Soumick Chatterjee, Franziska Gaidzik, Alessandro Sciarra, Hendrik Mattern, Gábor Janiga, Oliver Speck, Andreas Nürnberger, Sahani Pathiraja.

Model Description

Model Sources

• Repository: https://github.com/soumickmj/PULASki
• Paper: https://arxiv.org/abs/2312.15686

📄 License

This project is licensed under the Apache - 2.0 license.

📄 Citation

If you use this approach in your research or use codes from this repository or these weights, please cite the following in your publications:

BibTeX:

@article{chatterjee2023pulaski,
  title={PULASki: Learning inter-rater variability using statistical distances to improve probabilistic segmentation},
  author={Chatterjee, Soumick and Gaidzik, Franziska and Sciarra, Alessandro and Mattern, Hendrik and Janiga, G{\'a}bor and Speck, Oliver and N{\"u}rnberger, Andreas and Pathiraja, Sahani},
  journal={arXiv preprint arXiv:2312.15686},
  year={2023}
}

APA: Chatterjee, S., Gaidzik, F., Sciarra, A., Mattern, H., Janiga, G., Speck, O., Nuernberger, A., & Pathiraja, S. (2023). PULASki: Learning inter-rater variability using statistical distances to improve probabilistic segmentation. arXiv preprint arXiv:2312.15686.