TY - GEN
T1 - A Weakly Supervised Deep Learning Framework for COVID-19 CT Detection and Analysis
AU - Gozes, Ophir
AU - Frid-Adar, Maayan
AU - Sagie, Nimrod
AU - Kabakovitch, Asher
AU - Amran, Dor
AU - Amer, Rula
AU - Greenspan, Hayit
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - The outbreak of the COVID-19 global pandemic has affected millions and has a severe impact on our daily lives. To support radiologists in this overwhelming challenge, we developed a weakly supervised deep learning framework that can detect, localize, and quantify the severity of COVID-19 disease from chest CT scans using limited annotations. The framework is designed to rapidly provide a solution during the initial outbreak of a pandemic when datasets availability is limited. It is comprised of a pipeline of image processing algorithms which includes lung segmentation, 2D slice classification, and fine-grained localization. In addition, we present the Coronascore bio-marker which corresponds to the severity grade of the disease. Finally, we present an unsupervised feature space clustering which can assist in understanding the COVID-19 radiographic manifestations. We present our results on an external dataset comprised of 199 patients from Zhejiang province, China.
AB - The outbreak of the COVID-19 global pandemic has affected millions and has a severe impact on our daily lives. To support radiologists in this overwhelming challenge, we developed a weakly supervised deep learning framework that can detect, localize, and quantify the severity of COVID-19 disease from chest CT scans using limited annotations. The framework is designed to rapidly provide a solution during the initial outbreak of a pandemic when datasets availability is limited. It is comprised of a pipeline of image processing algorithms which includes lung segmentation, 2D slice classification, and fine-grained localization. In addition, we present the Coronascore bio-marker which corresponds to the severity grade of the disease. Finally, we present an unsupervised feature space clustering which can assist in understanding the COVID-19 radiographic manifestations. We present our results on an external dataset comprised of 199 patients from Zhejiang province, China.
KW - AI
KW - COVID-19
KW - Chest CT
KW - Corona
KW - Deep learning
KW - Lung
UR - http://www.scopus.com/inward/record.url?scp=85097092297&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-62469-9_8
DO - 10.1007/978-3-030-62469-9_8
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AN - SCOPUS:85097092297
SN - 9783030624682
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 84
EP - 93
BT - Thoracic Image Analysis - Second International Workshop, TIA 2020, Held in Conjunction with MICCAI 2020, Proceedings
A2 - Petersen, Jens
A2 - San José Estépar, Raúl
A2 - Schmidt-Richberg, Alexander
A2 - Gerard, Sarah
A2 - Lassen-Schmidt, Bianca
A2 - Jacobs, Colin
A2 - Beichel, Reinhard
A2 - Mori, Kensaku
PB - Springer Science and Business Media Deutschland GmbH
T2 - 2nd International Workshop on Thoracic Image Analysis, TIA 2020 Held in Conjunction with Medical Image Computing and Computer-Assisted Intervention Conference, MICCAI 2020
Y2 - 8 October 2020 through 8 October 2020
ER -
