Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+pipeline_tag: image-segmentation
+tags:
+- medical
+- probabilistic unet
+- 2D
+- PULASki
+- multiple sclerosis segmentation
+- multiple sclerosis
+- 3T FLAIR
+- FLAIR
+- MRI
+- 3T
+- multiple rater
+- Conditional VAE
+- distribution distance
+library_name: pytorch
+---
+# PULASki_ProbUNet2D_FID_MSSeg
+
+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.
+
+## Model Details
+
+It was introduced in [PULASki: Learning inter-rater variability using statistical distances to improve
+probabilistic segmentation](https://arxiv.org/abs/2312.15686) by Soumick Chatterjee, Franziska Gaidzik, Alessandro Sciarra, Hendrik Mattern, Gábor Janiga, Oliver Speck, Andreas Nürnberger, Sahani Pathiraja.
+
+### Model Description
+
+- **Developed by:** Dr Soumick Chatterjee
+- **Model type:** PULASki 2D Probabilistic UNet, trained with Fréchet inception distance (FID) loss
+- **Task:** Probabilistic multiple sclerosis (MS) segmentation in 3T MRI FLAIR volumes - provides 7 segmentations for each input volume
+- **Training dataset:** 3T FLAIR MRIs from the MS segmentation dataset of a MICCAI 2016 challenge, details mentioned in Sec. 4.1 of https://arxiv.org/pdf/2312.15686
+
+### Model Sources
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** https://github.com/soumickmj/PULASki
+- **Paper:** https://arxiv.org/abs/2312.15686
+
+## Citation
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+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:**
+
+```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.