Severity Quantification and Lesion Localization of COVID-19 on CXR using Vision Transformer

• URL: http://arxiv.org/abs/2103.07062v1
• Date: Fri, 12 Mar 2021 03:17:19 GMT
• Title: Severity Quantification and Lesion Localization of COVID-19 on CXR using Vision Transformer
• Authors: Gwanghyun Kim, Sangjoon Park, Yujin Oh, Joon Beom Seo, Sang Min Lee, Jin Hwan Kim, Sungjun Moon, Jae-Kwang Lim, Jong Chul Ye
• Abstract summary: Under the global pandemic of COVID-19, building an automated framework that quantifies the severity of COVID-19 has become increasingly important. We propose a novel Vision Transformer tailored for both quantification of the severity and clinically applicable localization of the COVID-19 related lesions. Our model is trained in a weakly-supervised manner to generate the full probability maps from weak array-based labels.
• Score: 25.144248675578286
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Under the global pandemic of COVID-19, building an automated framework that quantifies the severity of COVID-19 and localizes the relevant lesion on chest X-ray images has become increasingly important. Although pixel-level lesion severity labels, e.g. lesion segmentation, can be the most excellent target to build a robust model, collecting enough data with such labels is difficult due to time and labor-intensive annotation tasks. Instead, array-based severity labeling that assigns integer scores on six subdivisions of lungs can be an alternative choice enabling the quick labeling. Several groups proposed deep learning algorithms that quantify the severity of COVID-19 using the array-based COVID-19 labels and localize the lesions with explainability maps. To further improve the accuracy and interpretability, here we propose a novel Vision Transformer tailored for both quantification of the severity and clinically applicable localization of the COVID-19 related lesions. Our model is trained in a weakly-supervised manner to generate the full probability maps from weak array-based labels. Furthermore, a novel progressive self-training method enables us to build a model with a small labeled dataset. The quantitative and qualitative analysis on the external testset demonstrates that our method shows comparable performance with radiologists for both tasks with stability in a real-world application.

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