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Multi-modal Deep Learning
March 7, 2024, 5:45 a.m. | Chen Yuhua
cs.CV updates on arXiv.org arxiv.org
Abstract: This article investigates deep learning methodologies for single-modality clinical data analysis, as a crucial precursor to multi-modal medical research. Building on Guo JingYuan's work, the study refines clinical data processing through Compact Convolutional Transformer (CCT), Patch Up, and the innovative CamCenterLoss technique, establishing a foundation for future multimodal investigations. The proposed methodology demonstrates improved prediction accuracy and at tentiveness to critically ill patients compared to Guo JingYuan's ResNet and StageNet approaches. Novelty that using image-pretrained …
abstract analysis article arxiv building clinical cs.ai cs.cv data data analysis data processing deep learning eess.iv foundation future investigations medical medical research methodology modal multi-modal multimodal processing research study through transformer type work
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