MAGIC

<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>

MAGIC

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

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Original papers

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