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

Qinghua Zhou, Shuihua Wang, Xin Zhang*, Yu Dong Zhang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number106883
JournalComputer Methods and Programs in Biomedicine
Volume221
DOIs
Publication statusPublished - Jun 2022
Externally publishedYes

Keywords

  • Anomaly localisation
  • Pseudo data
  • Segmentation
  • Weakly supervision

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