2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDCOVID-IP-1.9
Paper Title BOOSTING DEEP TRANSFER LEARNING FOR COVID-19 CLASSIFICATION
Authors Fouzia Altaf, Syed M.S. Islam, Naeem K. Janjua, Edith Cowan University, Australia; Naveed Akhtar, University of Western Australia, Australia
SessionCOVID-IP-1: COVID Related Image Processing 1
LocationArea C
Session Time:Monday, 20 September, 15:30 - 17:00
Presentation Time:Monday, 20 September, 15:30 - 17:00
Presentation Poster
Topic COVID-Related Image Processing: COVID-related image processing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract 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 identifying contrasting observations in the previous studies.