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Conference Papers Year : 2021

A Deep Learning Approach for Improved Segmentation of Lesions Related to Covid-19 Chest CT Scans

Abstract

The current coronavirus pandemic (COVID-19) became a worldwide threat, infecting more than 42 million people since its outbreak in early 2020. Recent studies show that analyzing chest CT scans plays an essential role in assessing disease progression and facilitates early diagnosis. Automatic lesion segmentation constitutes a useful tool to complement more traditional healthcare system strategies to address the COVID-19 crisis. We introduce MASC-Net, a novel deep neural network that automatically detects COVID-19 related infected lung regions from chest CT scans. The proposed architecture consists of a multi-input encoder-decoder that aggregates high-level features extracted with variable-size receptive fields.
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Dates and versions

hal-03188444 , version 1 (02-04-2021)

Identifiers

  • HAL Id : hal-03188444 , version 1

Cite

Vlad Vasilescu, Ana Neacsu, Emilie Chouzenoux, Jean-Christophe Pesquet, Corneliu Burileanu. A Deep Learning Approach for Improved Segmentation of Lesions Related to Covid-19 Chest CT Scans. ISBI 2021 - IEEE International Symposium on Biomedical Imaging, Apr 2021, Nice / Virtual, France. ⟨hal-03188444⟩
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