Boosting deep transfer learning for COVID-19 classification
Syed Mohammed Shamsul Islam
ORCID : 0000-0002-3200-2903
ORCID : 0000-0003-0483-8196
2021 IEEE International Conference on Image Processing (ICIP)
School of Science
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.
Multidisciplinary biological approaches to personalised disease diagnosis, prognosis and management