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The work-flow of grad-CAM guided convolutional collaborative learning (gCAM-CCL), an end-to-end model for automated classification and interpretation for multimodal data fusion. Genetic data is fed into a ConvNet and then flattened to a fully connected layer. Brain functional connectivity (FC) data is fed into a deep network. A collaborative learning layer fuses the two deep networks and passes two composite gradients mutually during the back-propagation process.
