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Title: Towards Designing an Explainable COVID-19 Diagnosis System
Date: 26 August 2022
Time: 8:00 AM Eastern (New York time)
Duration: Approximately 1 Hour
Presenters: Dr. Yu-Huan Wu and Dr. Shanghua Gao
Based on the IEEE Xplore® article: JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation
Published: IEEE Transactions on Image Processing, February 2022, available in IEEE Xplore®
In the beginning of 2020, the coronavirus disease 2019 (COVID-19) has caused a pandemic disease in over 200 countries, affecting billions of humans. Identifying and separating the infected people during the early stage is the most important step in controlling
the pandemic. However, the main diagnostic tool, i.e., RT-PCR test, does not have high sensitivity for diagnosing COVID-19. However, chest CT is a valuable complementary tool in the early stage that offers high sensitivity. But chest CT test requires 21.5 minutes even for experienced radiologists that were severely lacking in the pandemic. Designing an automatic diagnosis system is highly desirable in solving this issue.
Based on the above motivation, we developed a novel Joint Classification and Segmentation (JCS) system to perform a real-time and explainable COVID-19 chest CT diagnosis. To enable the training of our system, we collected a large-scale COVID-19 classification and segmentation (COVID-CS) dataset, with 144K chest CT images of 400 COVID-19 patients and 350 uninfected cases. We annotated 3,855 chest CT images of 200 patients with fine-grained pixel-level labels of opacifications, which are increased attenuation of the lung parenchyma. We also have annotated lesion counts, opacification areas, and locations that benefit various diagnosis aspects. Extensive experiments demonstrate that the proposed JCS diagnosis system is very efficient for COVID-19 classification and segmentation. It obtains an average sensitivity of 95.0% and a specificity of 93.0% on the classification test set, and 78.5% Dice score on the segmentation test set of our COVID-CS dataset. The COVID-CS dataset and code are available at https://github.com/yuhuan-wu/JCS.
Yu-Huan Wu received the Bachelor’s degree from Xidian University in 2018. He is currently a fourth-year year Ph.D. candidate with the College of Computer Science, Nankai University, China, supervised by Prof. Ming-Ming Cheng.
His research interests include computer vision, medical imaging, and autonomous driving.
Dr. Wu has published 10+ papers in top-tier journals and conferences like IEEE TPAMI, TIP, CVPR, and ICCV.
Yhanghua Gao is a fourth-year Ph.D. candidate in Media Computing Lab at Nankai University, China. He is supervised by Prof. Ming-Ming Cheng.
His research interests include computer vision, architecture design, and representation learning.
Dr. Gao has published six papers as the first author on leading journals and conferences, such as IEEE TPAMI and CVPR, receiving more than 1900 paper citations.
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