Metadata-Version: 2.1
Name: magiccluster
Version: 0.0.3
Summary: Multi-scale semi-supervised clustering
Home-page: https://github.com/anbai106/MAGIC
Author: junhao.wen
Author-email: junhao.wen89@email.com
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
License-File: LICENSE

<h1 align="center">
  <a href="https://anbai106.github.io/MAGIC/">
    <img src="https://anbai106.github.io/MAGIC/images/magic.png" alt="magic logo" width="150" height="150">
  </a>
  <br/>
  MAGIC
</h1>

<p align="center"><strong>Multi-scAle heteroGeneity analysIs and Clustering</strong></p>

<p align="center">
  <a href="https://anbai106.github.io/MAGIC/">Documentation</a>
</p>

## `MLNI`
**MAGIC**, Multi-scAle heteroGeneity analysIs and Clustering, is a multi-scale semi-supervised clustering method that aims to derive robust clustering solutions across different scales for brain diseases.

> :warning: **The documentation of this software is currently under development**

## Citing this work
### If you use this software for clustering:
> Wen J., Varol E., Chand G., Sotiras A., Davatzikos C. (2020) **MAGIC: Multi-scale Heterogeneity Analysis and Clustering for Brain Diseases**. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science, vol 12267. Springer, Cham. https://doi.org/10.1007/978-3-030-59728-3_66

> Wen J., Varol E., Chand G., Sotiras A., Davatzikos C. (2022) **Multi-scale semi-supervised clustering of brain images: Deriving disease subtypes**. Medical Image Analysis, 2022. https://doi.org/10.1016/j.media.2021.102304 - [Link](https://www.sciencedirect.com/science/article/pii/S1361841521003492)


