Domain Adaptation Using Pseudo Labels for COVID-19 Detection
Runtian Yuan Qingqiu Li Junlin Hou Jilan Xu Yuejie Zhang Rui Feng Hao Chen
In response to the need for rapid and accurate COVID-19 diagnosis during the
global pandemic, we present a two-stage framework that leverages pseudo labels
for domain adaptation to enhance the detection of COVID-19 from CT scans. By
utilizing annotated data from one domain and non-annotated data from another,
the model overcomes the challenge of data scarcity and variability, common in
emergent health crises. The innovative approach of generating pseudo labels
enables the model to iteratively refine its learning process, thereby improving
its accuracy and adaptability across different hospitals and medical centres.
Experimental results on COV19-CT-DB database showcase the model's potential to
achieve high diagnostic precision, significantly contributing to efficient
patient management and alleviating the strain on healthcare systems. Our method
achieves 0.92 Macro F1 Score on the validation set of Covid-19 domain
adaptation challenge.