Pytoch implementation of rankCNN for brain disease prognosis
The code was written by Hezhe Qiao and Dr. Lin Chen, Department of Radiology at Chongqing Institute of Green\
and Intelligent Technology, Chinese Academy of Sciences.
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Introduction We proposed a ranking convolutional neural network (rankCNN) to address the prediction of MMSE through muti-classification. Specifically, we use a 3D convolutional neural network with sharing weights to extract the feature from MRI, followed by multiple sub-networks which transform the cognitive regression into a series of simpler binary classification. In addition, we further use a ranking layer measure the ranking information between samples to strengthen the ability of the classification by extracting more discriminative features.
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Prerequisites Linux python 3.7 Pytorch version 1.2.0 NVIDIA GPU + CUDA CuDNN (CPU mode, untested) Cuda version 10.0.61
3.Dataset The dataset used in this study was obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) that is avaiable at http://adni.loni.usc.edu/ . SID and IMAGE_ID in this file can be used to find the corresponding subjects and MRIs. MMSE at four time points are also avaiable in ADNIMERGE_ADNI1 and ADNIMERGE_ADNI1
4.Pre-process All MRIs were prepocessed by a standard pipeline in CAT12 toolbox which is avaiable at http://dbm,neuro.uni-jena.de/cat/.
5.Note Please cite our paper if you use this code in your own work. cite Qiao H, Chen L, Zhu F. Ranking convolutional neural network for Alzheimer's disease mini-mental state examination prediction at multiple time-points. Comput Methods Programs Biomed. 2022 Jan;213:106503. doi: 10.1016/j.cmpb.2021.106503. Epub 2021 Nov 6. PMID: 34798407