JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation

By Yu-Huan Wu, Shanghua Gao, Jie Mei, Jun Xu, Deng-Ping Fan, Chao Zhao, Ming-Ming Cheng. IEEE Transactions on Image Processing, 2020

445 citations recorded by the source corpus.

Recently, the coronavirus disease 2019 (COVID-19) has caused a pandemic disease in over 200 countries, influencing billions of humans. To control the infection, identifying and separating the infected people is the most crucial step. The main diagnostic tool is the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. Still, the sensitivity of the RT-PCR test is not high enough to effectively prevent the pandemic. The chest CT scan test provides a valuable complementary tool to the RT-PCR test, and it can identify the patients in the early-stage with high sensitivity. However, the chest CT scan test is usually time-consuming, requiring about 21.5 minutes per case. This paper develops a novel Joint Classification and Segmentation (JCS) system to perform real-time and explainable COVID- 19 chest CT diagnosis. To train our JCS system, we construct a large scale COVID- 19 Classification and Segmentation (COVID-CS) dataset, with 144,167 chest CT images of 400 COVID- 19 patients and 350 uninfected cases. 3,855 chest CT images of 200 patients are annotated 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 and thus 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.

Subfields: Infectious Diseases, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/8f361a48e1113b91ad4891c4c5b021367db62b31

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Concealed Object Detection (2021, background): “More details on this application can be found in the recent diagnosis system [9], segmentation model [8], and survey paper [89].”
Survey of Explainable AI Techniques in Healthcare (2023, background): “[52] lung Grad-CAM CT It shows a classifier based on the Res2Net network.”
PraNet: Parallel Reverse Attention Network for Polyp Segmentation (2020, methodology): “We hope this study will oﬀer the community an opportunity to explore more powerful models on the related topics such as lung infection segmentation [9]/classiﬁcation [28], or even on the upstream task, etc .”
Camouflaged Object Detection (2020, background): “, polyp segmentation [14], lung infection segmentation [18, 67]), agriculture (e.”
U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications (2020, methodology): “[382] have implemented a hybrid network with U-net for segmentation and a classifier for classification, while Yan et al.”
Medical Image Segmentation Review: The Success of U-Net (2022, methodology): “JCS [79] is a two branches architecture, which utilizes a Group Atrous (GA) module, in its segmentation branches bottleneck for feature modiﬁcation.”
RGB-D salient object detection: A survey (2020, background): “SOD plays a key role in a range of real-world applications, such as stereo matching [11], image understanding [12], co-saliency detection [13], action recognition [14], video detection and segmentation [15]–[18], semantic segmentation [19], [20], medical image segmentation [21]–[23], object tracking”