2020/06/16 by Wenxing Hu, Xiang-He Meng, Hu, Wenxing +19 · 8 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · Neuroscience · Psychology · #Artificial intelligence #Bioinformatics and Genomic Networks #Cell Image Analysis Techniques #Class (philosophy) #Cognition #Computer Vision and Pattern Recognition (cs.CV) #Computer science #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Electrical engineering #Feature (linguistics) #Functional Brain Connectivity Studies #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Machine learning #Mechanism (biology) #Neuroimaging #Neurons and Cognition (q-bio.NC) #Neuroscience #Pattern recognition (psychology) #Perspective (graphical) #Psychology #cs.CV #cs.LG #eess.IV #electronic engineering #information engineering #q-bio.NC
paper · pdf · doi:10.48550/arxiv.2006.09454
published in arXiv (Cornell University) (Cornell University)
arxiv created 2020/06/16 · openalex publication_date 2020/06/16 · arxiv updated 2020/06/18 · openalex created_date 2020/06/25 · openalex updated_date 2026/07/28
Multimodal fusion benefits disease diagnosis by providing a more comprehensive perspective. Developing algorithms is challenging due to data heterogeneity and the complex within- and between-modality associations. Deep-network-based data-fusion models have been developed to capture the complex associations and the performance in diagnosis has been improved accordingly. Moving beyond diagnosis prediction, evaluation of disease mechanisms is critically important for biomedical research. Deep-network-based data-fusion models, however, are difficult to interpret, bringing about difficulties for studying biological mechanisms. In this work, we develop an interpretable multimodal fusion model, namely gCAM-CCL, which can perform automated diagnosis and result interpretation simultaneously. The gCAM-CCL model can generate interpretable activation maps, which quantify pixel-level contributions of the input features. This is achieved by combining intermediate feature maps using gradient-based weights. Moreover, the estimated activation maps are class-specific, and the captured cross-data associations are interest/label related, which further facilitates class-specific analysis and biological mechanism analysis. We validate the gCAM-CCL model on a brain imaging-genetic study, and show gCAM-CCL's performed well for both classification and mechanism analysis. Mechanism analysis suggests that during task-fMRI scans, several object recognition related regions of interests (ROIs) are first activated and then several downstream encoding ROIs get involved. Results also suggest that the higher cognition performing group may have stronger neurotransmission signaling while the lower cognition performing group may have problem in brain/neuron development, resulting from genetic variations.