Boosting Deep Transfer Learning for COVID-19 Classification

Fouzia Altaf, Syed Islam, Naeem Janjua, Naveed Akhtar

Research output: Chapter in Book/Conference paperConference paperpeer-review

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 with identifying contrasting observations in the previous studies.
Original languageEnglish
Title of host publication2021 IEEE International Conference on Image Processing (ICIP)
Place of PublicationUSA
PublisherIEEE, Institute of Electrical and Electronics Engineers
ISBN (Electronic)978-1-6654-4115-5
DOIs
Publication statusPublished - 2021
Event2021 IEEE International Conference on Image Processing: Imaging Without Borders - Virtual, United States
Duration: 19 Sep 202122 Sep 2021

Conference

Conference2021 IEEE International Conference on Image Processing
Abbreviated titleICIP 2021
Country/TerritoryUnited States
Period19/09/2122/09/21

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