Title

Self-supervision and Multi-task Learning: Challenges in Fine-Grained COVID-19 Multi-class Classification from Chest X-rays

Document Type

Conference Proceeding

Publication Title

26th Annual Conference on Medical Image Understanding and Analysis 2022

Abstract

Quick and accurate diagnosis is of paramount importance to mitigate the effects of COVID-19 infection, particularly for severe cases. Enormous effort has been put towards developing deep learning methods to classify and detect COVID-19 infections from chest radiography images. However, recently some questions have been raised surrounding the clinical viability and effectiveness of such methods. In this work, we investigate the impact of multi-task learning (classification and segmentation) on the ability of CNNs to differentiate between various appearances of COVID-19 infections in the lung. We also employ self-supervised pre-training approaches, namely MoCo and inpainting-CXR, to eliminate the dependence on expensive ground truth annotations for COVID-19 classification. Finally, we conduct a critical evaluation of the models to assess their deploy-readiness and provide insights into the difficulties of fine-grained COVID-19 multi-class classification from chest X-rays. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

First Page

234

Last Page

250

DOI

10.1007/978-3-031-12053-4_18

Publication Date

7-25-2022

Keywords

Classification, COVID-19, Multi-task learning, Self-supervision, X-ray, Classification (of information), Classifiers, Deep learning, Learning systems, X ray radiography

Comments

IR Deposit conditions: non-described

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