This document describes the development of an AI model for diagnosing and quantifying the severity of COVID-19 using chest X-rays. The model uses a multi-task Vision Transformer trained on a large dataset of chest X-rays to learn common lung abnormalities. It achieves state-of-the-art performance in both diagnosing COVID-19 and quantifying severity. The model resembles the diagnostic process of clinical experts and its multi-task learning approach enhances its generalization capabilities. Possible applications of the AI model include using chest X-rays to screen patients before PCR testing and to help triage patients in healthcare facilities.

1. 코로나19 진단 및 중증도 정량화를 위한
다중학습 기반의 AI 모델 개발 사례
박상준 박사과정
M.D., Radiation Oncologist
BISPL - BioImaging, Signal Processing, and Learning Lab.
Dept. of Bio and Brain Engineering
KAIST, Korea

2. COVID-19 pandemic (2019.12 – current)
[1] “Global COVID-19 dashboard”, WHO

3. Diagnosis of COVID-19
RT-PCR Chest CT Chest X-ray
Time > 4 hours > 30 min < 5 min
Sensitivity (%) >90 >90 69
Cost $ $$$ $
CXR abnormalities were detectable in 9% of patients whose initial
RT-PCR was negative.
[1] Fang et al.; Wong et al. Radiology, 2020

5. Characteristics of COVID-19 (mild)

6. Characteristics of COVID-19 (severe)

7. 감염병 의심 환자
검체 채취
Chest X-ray
인공지능 기반의
진단, 정량화 기술
표준 검체 검사
감염병 진단
중증도 정량화
최소 4시간 소요
대기 시간을 활용하여 선별 검사 병행
병원
• 응급실에서 환자를 받아들이기 전
• RT-PCR 검사 시행 후, 자택 대기 통보 전
• Chest X-ray 영상 기반 선제 격리 여부 결정
• 병원 내 감염의 확률을 낮춤
생활치료센터
• 경증 확진 환자들의 급작스러운 병세 악화
• 영상 판독 전문의가 상주할 수 없는 경우
• 중증 진행여부를 Chest X-ray 영상 기반 선별
• 선별 영상을 우선적으로 판독 전문의에게 검사

8. Purpose
We aimed to develop a robust algorithm for diagnosis and severity quantification
of COVID-19 by utilizing pre-built large data for common CXR findings to achieve
excellent generalization performance.

9. Data collection

10. Dataset for COVID-19 diagnosis and classification

11. Training scheme
CheXpert dataset
 224,316 CXRs from
65,240 patients
 14 classes
Low-level features
 Lung opacity
 Consolidation
 Pneumonia
 Pleural effusion
 Edema
 …
Train feature extractor
(encoder)
 3 public data
 4 institutional data
 COVID-19 diagnosis
 COVID-19 severity
(1)
COVID-19 dataset
Used as encoder of hybrid
ViT model

12. Backbone network to extract low-level feature corpus
Park and Kim et al. Under review (2021)

13. Proposed Multi-task ViT model
Park and Kim et al. Under review (2021)

14. Procedure of severity prediction and labeling
Park and Kim et al. Under review (2021)

15. Analogy between diagnosis by expert and by our method

16. Multi-task model vs. Single-task model
Park and Kim et al. Under review (2021)
Classification performance
Severity quantification performance

17. Detailed model performances: multi-task model
Park and Kim et al. Under review (2021)
Severity quantification performance
Classification performance

18. Visualization results: classification

19. Comparison with other models
Park and Kim et al. Under review (2021)

20. Visualization results: severity quantification
Park and Kim et al. Under review (2021)

21. Visualization results: comparison with radiologist
Park and Kim et al. Under review (2021)

22. Conclusion
Our Vision Transformer-based model, which resemble diagnostic procedure by
clinical expert, achieved the state-of-the-art performance in both diagnosis and
severity quantification tasks with superior generalization capacity, which was
further enhanced with multi-task learning of two relevant tasks.

23. Possible application of AI for COVID-19 management

24. Thank you!

25. Q & A