Domain Adaptation Using Pseudo Labels for COVID-19 Detection
This work addresses the need for rapid and accurate COVID-19 diagnosis in healthcare systems, though it is incremental as it builds on existing domain adaptation techniques.
The paper tackles the problem of COVID-19 detection from CT scans by developing a two-stage domain adaptation framework using pseudo labels to address data scarcity and variability, achieving a Macro F1 Score of 0.92 on a validation set.
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.