Paper Image

Distilling multimodal COVID-19 data for robust predictions

Published on:

2 November 2023

Primary Category:

Image and Video Processing

Paper Authors:

Yingying Fang,

Shuang Wu,

Sheng Zhang,

Chaoyan Huang,

Tieyong Zeng,

Xiaodan Xing,

Simon Walsh,

Guang Yang


Key Details

Proposes a dynamic multimodal information bottleneck framework for fusing data

Employs techniques from information theory to distill predictive signals

Demonstrates superior performance on COVID-19 mortality prediction

Significantly more robust to noise and missing data than other methods

Achieves state-of-the-art results on biomedical multimodal classification tasks

AI generated summary

Distilling multimodal COVID-19 data for robust predictions

This paper proposes a deep learning approach to fuse multimodal medical data like images and clinical records. It introduces techniques to filter out redundant and noisy information, while retaining predictive signals. On COVID-19 and other biomedical datasets, the method achieves state-of-the-art performance and is highly robust to missing data.

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