1.1k citations · 1.8k across the 32 of their papers we have counts for
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Multimodal Machine Learning in Image-Based and Clinical Biomedicine: Survey and Prospects
Elisa Warner, Joonsang Lee, William Hsu +4
Machine learning (ML) applications in medical artificial intelligence (AI) systems have shifted from traditional and statistical methods to increasing application of deep learning…
MaxCorrMGNN: A Multi-Graph Neural Network Framework for Generalized Multimodal Fusion of Medical Data for Outcome Prediction
Niharika S. D'Souza, Hongzhi Wang, Andrea Giovannini +4
With the emergence of multimodal electronic health records, the evidence for an outcome may be captured across multiple modalities ranging from clinical to imaging and genomic data…
Fusing Modalities by Multiplexed Graph Neural Networks for Outcome Prediction in Tuberculosis
Niharika S. D'Souza, Hongzhi Wang, Andrea Giovannini +4
In a complex disease such as tuberculosis, the evidence for the disease and its evolution may be present in multiple modalities such as clinical, genomic, or imaging data. Effectiv…
Addressing Deep Learning Model Uncertainty in Long-Range Climate Forecasting with Late Fusion
Ken C. L. Wong, Hongzhi Wang, Etienne E. Vos +3
Global warming leads to the increase in frequency and intensity of climate extremes that cause tremendous loss of lives and property. Accurate long-range climate prediction allows…
Multi-modality fusion using canonical correlation analysis methods: Application in breast cancer survival prediction from histology and genomics
Vaishnavi Subramanian, Tanveer Syeda-Mahmood, Minh N. Do
The availability of multi-modality datasets provides a unique opportunity to characterize the same object of interest using multiple viewpoints more comprehensively. In this work,…
Multimodal fusion using sparse CCA for breast cancer survival prediction
Vaishnavi Subramanian, Tanveer Syeda-Mahmood, Minh N. Do
Effective understanding of a disease such as cancer requires fusing multiple sources of information captured across physical scales by multimodal data. In this work, we propose a n…
Classification of radiology reports by modality and anatomy: A comparative study
Marina Bendersky, Joy Wu, Tanveer Syeda-Mahmood
Data labeling is currently a time-consuming task that often requires expert knowledge. In research settings, the availability of correctly labeled data is crucial to ensure that mo…