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

Bullets

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.

Answers from this paper

Comments

No comments yet, be the first to start the conversation...

Sign up to comment on this paper

Sign Up