Boosting Deep Transfer Learning for COVID-19 Classification
This work addresses the challenge of accurate COVID-19 diagnosis for medical applications, but it is incremental as it builds on existing transfer learning approaches.
The paper tackled the problem of improving COVID-19 classification from chest CT scans with limited annotated data by proposing a novel model augmentation technique, resulting in a considerable performance boost over vanilla transfer learning methods.
COVID-19 classification using chest Computed Tomography (CT) has been found pragmatically useful by several studies. Due to the lack of annotated samples, these studies recommend transfer learning and explore the choices of pre-trained models and data augmentation. However, it is still unknown if there are better strategies than vanilla transfer learning for more accurate COVID-19 classification with limited CT data. This paper provides an affirmative answer, devising a novel `model' augmentation technique that allows a considerable performance boost to transfer learning for the task. Our method systematically reduces the distributional shift between the source and target domains and considers augmenting deep learning with complementary representation learning techniques. We establish the efficacy of our method with publicly available datasets and models, along with identifying contrasting observations in the previous studies.