ArticleQuantitative imaging in medicine and surgery2022
Development and validation of bone-suppressed deep learning classification of COVID-19 presentation in chest radiographs.
Article in Quantitative imaging in medicine and surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 7 citations in OpenAlex.
- GAN-based bone suppression using a combined loss function.Frontiers in artificial intelligence · 2026Article
- End-to-end deep learning radiomics: development and validation of a novel attention-based aggregate convolutional neural network to distinguish breast diffuse large B-cell lymphoma from breast invasive ductal carcinoma.Quantitative imaging in medicine and surgery · 2023Article
- Deep learning attention-guided radiomics for COVID-19 chest radiograph classification.Quantitative imaging in medicine and surgery · 2023Article
- Artificial intelligence-assisted multistrategy image enhancement of chest X-rays for COVID-19 classification.Quantitative imaging in medicine and surgery · 2023Article
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Authors and funding
17 authors at 5 institutions in 2 countries.
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Abstract
Background: Coronavirus disease 2019 (COVID-19) is a pandemic disease. Fast and accurate diagnosis of COVID-19 from chest radiography may enable more efficient allocation of scarce medical resources and hence improved patient outcomes. Deep learning classification of chest radiographs may be a plausible step towards this. We hypothesize that bone suppression of chest radiographs may improve the performance of deep learning classification of COVID-19 phenomena in chest radiographs. Methods: Two bone suppression methods (Gusarev Results: Bone suppression of external test data was found to significantly (P<0.05) improve AUC for one classifier architecture [from 0.698 (non-suppressed) to 0.732 (Rajaraman-suppressed)]. For the other classifier architecture, suppression did not significantly (P>0.05) improve or worsen classifier performance. Conclusions: Rajaraman suppression significantly improved classification performance in one classification architecture, and did not significantly worsen classifier performance in the other classifier architecture. This research could be extended to explore the impact of bone suppression on classification of different lung pathologies, and the effect of other image enhancement techniques on classifier performance.
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