Dec 22, 2021 · We propose a multi-modal attention module which use EHR data to help the selection of important regions during image feature extraction process.
For a better exploiting of image and EHR data, we propose a multi- modal attention module which use EHR data to help the selection of important regions during ...
A multi-modal attention module which use EHR data to help the selection of important regions during image feature extraction process conducted by ...
Moreover, we propose to incorporate multi-head machnism to gated multimodal unit (GMU) to make it able to parallelly fuse image and EHR features in different ...
Oct 16, 2020 · In this paper, we describe different data fusion techniques that can be applied to combine medical imaging with EHR, and systematically review medical data ...
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In this paper, we describe different data fusion techniques that can be applied to combine medical imaging with EHR, and systematically review medical data ...
Missing: head machanisms.
Jun 23, 2023 · Two multi-head cross attention to interactively fuse information from images and metadata. [AUC] Images: 0.944 clinical features: 0.964 images + ...
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Multimodal biomedical data fusion, a fundamental exemplar of data mining techniques, coalesces multifarious information modalities in biomedical domain, such as ...
MedFuseNet: An attention-based multimodal deep learning model for ...
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Oct 6, 2021 · In this paper, we develop MedFuseNet, an attention-based multimodal deep learning model, for VQA on medical images taking the associated challenges into ...
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We present a novel framework based on hypernetworks to fuse clinical imaging and tabular data by conditioning the image processing on the EHR's values and ...