fMRI_SSPNet-An-interpretable-3D-CNN-for-classifying-schizophrenia

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fMRI_SSPNet-An-interpretable-3D-CNN-for-classifying-schizophrenia

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"SSPNet: An interpretable 3D-CNN for classification of schizophrenia using phase maps of resting-state complex-valued fMRI data," published in Medical Image Analysis

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"SSPNet: An interpretable CNN for classification of schizophrenia using phase maps of resting-state complex-valued fMRI data," published in Medical Image Analysis

Requirements:
         Python 3.6.13   Keras 2.4.3

Datatsets:
        Our sample sets can be downloaded from https://pan.baidu.com/s/1mhppL7V9rd224XLwIezdPQ (password: iin0).
        
        (1) SSPsamples.mat: sample set of DMN spatial source phase separated from complex-valued fMRI data.
        (2) SSMsamples.mat: sample set of DMN spatial source magnitude separated from complex-valued fMRI data.
        (3) MAGsamples.mat: sample set of DMN separated from magnitude-only fMRI data.

Experimental steps:    
   
        (1) SSPNet_trainingCode.py to train a 3D-CNN model using sample set of DMN spatial source phase separated from complex-valued fMRI data. 
        Note: Three models trained by spatial source phase, spatial source magnitude and magnitude-only data are stored in model\SSPNet_weights.h5, SSMNet_weights.h5 and MAGNet_weights.h5.

(2) main.py to perform classification and generate interpretability results (testing code).

fMRI_SSPNet-An-interpretable-3D-CNN-for-classifying-schizophrenia

fMRI_SSPNet-An-interpretable-3D-CNN-for-classifying-schizophrenia Latest Version Download

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fMRI_SSPNet-An-interpretable-3D-CNN-for-classifying-schizophrenia

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