Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification from CT Images

TL;DR

This paper introduces a weakly supervised deep learning approach for detecting and classifying COVID-19 infections from CT images, reducing the need for manual labeling while maintaining high accuracy, aiding rapid diagnosis during the pandemic.

Contribution

The study presents a novel weakly supervised deep learning method that accurately detects COVID-19 from CT scans with minimal manual annotation, addressing resource constraints.

Findings

Achieved high accuracy in COVID-19 detection from CT images.

Reduced manual labeling requirements for training deep learning models.

Demonstrated potential for large-scale clinical deployment.

Abstract

An outbreak of a novel coronavirus disease (i.e., COVID-19) has been recorded in Wuhan, China since late December 2019, which subsequently became pandemic around the world. Although COVID-19 is an acutely treated disease, it can also be fatal with a risk of fatality of 4.03% in China and the highest of 13.04% in Algeria and 12.67% Italy (as of 8th April 2020). The onset of serious illness may result in death as a consequence of substantial alveolar damage and progressive respiratory failure. Although laboratory testing, e.g., using reverse transcription polymerase chain reaction (RT-PCR), is the golden standard for clinical diagnosis, the tests may produce false negatives. Moreover, under the pandemic situation, shortage of RT-PCR testing resources may also delay the following clinical decision and treatment. Under such circumstances, chest CT imaging has become a valuable tool for both diagnosis and prognosis of COVID-19 patients. In this study, we propose a weakly supervised deep learning strategy for detecting and classifying COVID-19 infection from CT images. The proposed method can minimise the requirements of manual labelling of CT images but still be able to obtain accurate infection detection and distinguish COVID-19 from non-COVID-19 cases. Based on the promising results obtained qualitatively and quantitatively, we can envisage a wide deployment of our developed technique in large-scale clinical studies.