Name
Title | ||
|---|---|---|
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 world-wide 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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ISBI48211.2021.9434139 | 2021 IEEE 18TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI) |
Keywords | DocType | ISSN |
COVID-19, Computer'Tomography, lesion segmentation | Conference | 1945-7928 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vlad Vasilescu | 1 | 1 | 0.35 |
| Ana Neacsu | 2 | 1 | 1.70 |
| Emilie Chouzenoux | 3 | 202 | 26.37 |
| Jean-Christophe Pesquet | 4 | 18 | 11.52 |
| Corneliu Burileanu | 5 | 69 | 23.01 |