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Collapse Classification in Lung Ultrasound Images for COVID-19 Induced Pneumonia
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  • Nabeel Durrani ,
  • Damjan Vukovic ,
  • Maria Antico ,
  • Jeroen van der Burgt ,
  • Ruud JG van van Sloun ,
  • Libertario Demi ,
  • Andrew Wang ,
  • Kavi Haji ,
  • Jason Dowling ,
  • Girija Chetty ,
  • Davide Fontanarosa ,
  • Marian Steffens
Nabeel Durrani
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Damjan Vukovic
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Maria Antico
Queensland university of Technology, Queensland university of Technology, Queensland university of Technology, Queensland university of Technology

Corresponding Author:[email protected]

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Jeroen van der Burgt
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Ruud JG van van Sloun
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Libertario Demi
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Andrew Wang
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Kavi Haji
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Jason Dowling
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Girija Chetty
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Davide Fontanarosa
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Marian Steffens
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Our automated deep learning-based approach identifies consolidation/collapse in LUS images to aid in the diagnosis of late stages of COVID-19 induced pneumonia, where consolidation/collapse is one of the possible associated pathologies. A common challenge in training such models is that annotating each frame of an ultrasound video requires high labelling effort. This effort in practice becomes prohibitive for large ultrasound datasets. To understand the impact of various degrees of labelling precision, we compare labelling strategies to train fully supervised models (frame-based method, higher labelling effort) and inaccurately supervised models (video-based methods, lower labelling effort), both of which yield binary predictions for LUS videos on a frame-by-frame level. We moreover introduce a novel sampled quaternary method which randomly samples only 10% of the LUS video frames and subsequently assigns (ordinal) categorical labels to all frames in the video based on the fraction of positively annotated samples. This method outperformed the inaccurately supervised video-based method of our previous work on pleural effusions. More surprisingly, this method outperformed the supervised frame-based approach with respect to metrics such as precision-recall area under curve (PR-AUC) and F1 score that are suitable for the class imbalance scenario of our dataset despite being a form of inaccurate learning. This may be due to the combination of a significantly smaller data set size compared to our previous work and the higher complexity of consolidation/collapse compared to pleural effusion, two factors which contribute to label noise and overfitting; specifically, we argue that our video-based method is more robust with respect to label noise and mitigates overfitting in a manner similar to label smoothing. Using clinical expert feedback, separate criteria were developed to exclude data from the training and test sets respectively for our ten-fold cross validation results, which resulted in a PR-AUC score of 73% and an accuracy of 89%. While the efficacy of our classifier using the sampled quaternary method must be verified on a larger consolidation/collapse dataset, when considering the complexity of the pathology, our proposed classifier using the sampled quaternary video-based method is clinically comparable with trained experts and improves over the video-based method of our previous work on pleural effusions.
20 Oct 2022Published in Scientific Reports volume 12 issue 1. 10.1038/s41598-022-22196-y