A Weakly Supervised Deep Learning Framework for COVID-19 CT Detection and Analysis

Ophir Gozes, Maayan Frid-Adar, Nimrod Sagie, Asher Kabakovitch, Dor Amran, Rula Amer, Hayit Greenspan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationThoracic Image Analysis - Second International Workshop, TIA 2020, Held in Conjunction with MICCAI 2020, Proceedings
EditorsJens Petersen, Raúl San José Estépar, Alexander Schmidt-Richberg, Sarah Gerard, Bianca Lassen-Schmidt, Colin Jacobs, Reinhard Beichel, Kensaku Mori
PublisherSpringer Science and Business Media Deutschland GmbH
Pages84-93
Number of pages10
ISBN (Print)9783030624682
DOIs
StatePublished - 2020
Event2nd International Workshop on Thoracic Image Analysis, TIA 2020 Held in Conjunction with Medical Image Computing and Computer-Assisted Intervention Conference, MICCAI 2020 - Lima, Peru
Duration: 8 Oct 20208 Oct 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12502 LNCS

Conference

Conference2nd International Workshop on Thoracic Image Analysis, TIA 2020 Held in Conjunction with Medical Image Computing and Computer-Assisted Intervention Conference, MICCAI 2020
Country/TerritoryPeru
CityLima
Period8/10/208/10/20

Keywords

  • AI
  • COVID-19
  • Chest CT
  • Corona
  • Deep learning
  • Lung

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

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