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Fouzia Altaf

Overview of thesis

Deep Learning has provided many breakthroughs in the field of Computer Vision. Medical image analysis has always been the major beneficiary of the developments in Computer Vision. However, application of deep learning in medical image analysis is often handicapped due to the limited amount of annotated training data. For medical images, annotation of data for disease detection is often tedious and expensive. Moreover, the available training samples for a given task are generally scarce and imbalanced. These conditions are not conducive for medical image analysis with deep learning. This research has been developing techniques to leverage knowledge from other domains, e.g. natural images, for medical image analysis using deep learning. Besides advancing our knowledge in machine learning, this research is relevant to medical imaging industry. It is expected to have a downstream impact on the community through improved automated healthcare facilities.

Qualifications

  • Master of Philosophy, University of the Punjab Lahore, Pakistan (2010-2012).

Research

Research Interests

  • Deep Learning
  • Medical Image Analysis
  • Dictionary Learning

Past Research employment history

  • 2014: Visiting Researcher, The University of Western Australia.

Scholarships and Awards

  • 2019-2022, The Australian Government Research Training Program (RTP) Scholarship.
  • 2021-Digital Image Computing: Techniques and Applications (DICTA), Best paper runner-up Award for Machine Learning in Medical Image Analysis.

Supervisors

Dr Syed Mohammad Shamsul Islam, School of Science, ECU.
Dr Naeem Khalid Janjua, School of Science, ECU.

Contact

Fouzia Altaf
PhD Student
School of Science
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