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WVALE: Weak variational autoencoder for localisation and enhancement of COVID-19 lung infections

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journal contribution
posted on 2023-08-04, 12:27 authored by Q Zhou, S Wang, X Zhang, YD Zhang
Background and objective: The COVID-19 pandemic is a major global health crisis of this century. The use of neural networks with CT imaging can potentially improve clinicians’ efficiency in diagnosis. Previous studies in this field have primarily focused on classifying the disease on CT images, while few studies targeted the localisation of disease regions. Developing neural networks for automating the latter task is impeded by limited CT images with pixel-level annotations available to the research community. Methods: This paper proposes a weakly-supervised framework named “Weak Variational Autoencoder for Localisation and Enhancement” (WVALE) to address this challenge for COVID-19 CT images. This framework includes two components: anomaly localisation with a novel WVAE model and enhancement of supervised segmentation models with WVALE. Results: The WVAE model have been shown to produce high-quality post-hoc attention maps with fine borders around infection regions, while weak supervision segmentation shows results comparable to conventional supervised segmentation models. The WVALE framework can enhance the performance of a range of supervised segmentation models, including state-of-art models for the segmentation of COVID-19 lung infection. Conclusions: Our study provides a proof-of-concept for weakly supervised segmentation and an alternative approach to alleviate the lack of annotation, while its independence from classification & segmentation frameworks makes it easily integrable with existing systems.


Royal Society International Exchanges Cost Share Award, UK (RP202G0230)

Driving innovation in precision medicine through translational life sciences research at the University of Leicester

UK Research and Innovation

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Hope Foundation for Cancer Research, UK (RM60G0680)

Accelerator Award (round 1)

British Heart Foundation

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Sino-UK Industrial Fund (RP202G0289)

Global Challenges Research Fund (GCRF), UK (P202PF11)

LIAS Pioneering Partnerships award, UK (P202ED10)

Data Science Enhancement Fund, UK (P202RE237 )


Author affiliation

School of Computing and Mathematical Sciences, University of Leicester


  • AM (Accepted Manuscript)

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Computer Methods and Programs in Biomedicine









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