Multi-scale semi-supervised clustering
Project description
MAGIC
Multi-scAle heteroGeneity analysIs and Clustering
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
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for magiccluster-0.0.2-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 768b7750806e308a1e4e26468ed615607e1101a8a732007dfdb705c1b0f2712a |
|
MD5 | e67a1a91a2eabdb5b6d822a8bbf5a567 |
|
BLAKE2b-256 | 89c8f68116adb00790f01632a574833ca00a594f188127df0c6b3fb4d88692f5 |