Interpretable Fusion Analytics Framework for fMRI Connectivity: Self-Attention Mechanism and Latent Space Item-Response Model

@article{Kim2022InterpretableFA,
  title={Interpretable Fusion Analytics Framework for fMRI Connectivity: Self-Attention Mechanism and Latent Space Item-Response Model},
  author={Jeongjae Kim and Yeseul Jeon and Sumin Yu and Junggu Choi and Sanghoon Han},
  journal={ArXiv},
  year={2022},
  volume={abs/2207.01581}
}
There have been several attempts to use deep learning based on brain fMRI signals to classify cognitive impairment diseases. However, deep learning is a hidden black box model that makes it difficult to interpret the process of classification. To address this issue, we propose a novel analytical framework that interprets the classification result from deep learning processes. We first derive the region of interest (ROI) functional connectivity network (FCN) by embedding functions based on their… 

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