Representation Learning with Information Theory to Detect COVID-19 and its Severity

Research output: Chapter in Book/Report/Conference proceedingChapterResearchpeer-review


Successful data representation is a fundamental factor in machine learning based medical imaging analysis. Deep Learning (DL) has taken an essential role in robust representation learning. However, the inability of deep models to generalize to unseen data can quickly overfit intricate patterns. Thereby, the importance of implementing strategies to aid deep models in discovering useful priors from data to learn their intrinsic properties. Our model, which we call a dual role network (DRN), uses a dependency maximization approach based on Least Squared Mutual Information (LSMI). LSMI leverages dependency measures to ensure representation invariance and local smoothness. While prior works have used information theory dependency measures like mutual information,
these are known to be computationally expensive due to the density estimation step. In contrast, our proposed DRN with LSMI formulation does not require the density estimation step and can be used as an alternative to approximate mutual information. Experiments on the CT based COVID-19 Detection and COVID-19 Severity Detection Challenges of the 2nd COV19D competition [24] demonstrate the effectiveness of our method compared to the baseline method of such competition.
Original languageEnglish
Title of host publicationLecture Notes in Computer Science
Subtitle of host publicationComputer Vision – ECCV 2022 Workshops
EditorsLeonid Karlinsky, Tomer Michaeli, Ko Nishino
PublisherSpringer, Cham
Number of pages16
ISBN (Electronic)978-3-031-25082-8
ISBN (Print)978-3-031-25081-1
Publication statusPublished - 23 Feb 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13807 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Bibliographical note

Funding Information:
Acknowledgement. We want to thank the organizers of the 2nd COV19D Competition occurring in the ECCV 2022 Workshop: AI-enabled Medical Image Analysis - Digital Pathology & Radiology/COVID19 for providing access to extensive and high-quality data to benchmark our model. This research has been partially financed by the European Union under the Horizon 2020 Research and Innovation programme under grant agreement 101016131 (ICOVID).

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.


  • Representation learning
  • mutual information
  • COVID-19 detection


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