Fusing modalities by multiplexed graph neural networks for outcome prediction from medical data and beyond-Reference-Cited by-同舟云学术

Fusing modalities by multiplexed graph neural networks for outcome prediction from medical data and beyond

Published:2024-04 Issue: Volume:93 Page:103064
ISSN:1361-8415
Container-title:Medical Image Analysis
language:en
Short-container-title:Medical Image Analysis

Author:

D‘Souza Niharika S.,Wang Hongzhi,Giovannini Andrea,Foncubierta-Rodriguez Antonio^ORCID,Beck Kristen L.,Boyko Orest^ORCID,Syeda-Mahmood Tanveer F.^ORCID

Publisher

Elsevier BV

Subject

Computer Graphics and Computer-Aided Design,Health Informatics,Computer Vision and Pattern Recognition,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Reference62 articles.

1. Multimodal machine learning: A survey and taxonomy;Baltrušaitis;IEEE Trans. Pattern Anal. Mach. Intell.,2018

2. A tensor-based unified approach for clustering coefficients in financial multiplex networks;Bartesaghi;Inform. Sci.,2022

3. Kernel methods for mining instance data in ontologies;Bloehdorn,2007

4. Deep learning with multimodal representation for pancancer prognosis prediction;Cheerla;Bioinformatics,2019

5. Pathomic fusion: an integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis;Chen;IEEE Trans. Med. Imaging,2020

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