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  • See Also
  • ORCiD profile
    https://orcid.org/0000-0002-9557-8889
  • ORCiD profile
  • Google Scholar profile
    https://scholar.google.com/citations?user=UsPFQ-kAAAAJ&hl=en
  • Google Scholar profile

Abid Ishaq

Overview of thesis

My thesis aims to develop a deep learning-based approach for accurate segmentation of 3D medical images, which is critical for diagnosis and treatment planning. The method involves training a 3D convolutional neural network, such as 3D U-Net, on volumetric imaging data (e.g., MRI or CT scans). By leveraging spatial context in three dimensions and advanced training techniques like data augmentation and patch-based learning, the model is expected to achieve high segmentation performance. The anticipated outcome is a robust and generalizable segmentation model that improves upon traditional methods in both precision and clinical applicability.

Qualifications

  • MS Computer Science, Khwaja Fareed University of Engineering and Information Technology, Pakistan, 2020.
  • MSc Telecommunications, Government College University, Lahore, Pakistan, 2009.

Research

Research Interests

  • Health Informatics,
  • Computer Vision
  • Ensemble Learning

Past Teaching

  • Lecturer Computer Science and IT, The Islamia University of Bahawalpur, Pakistan.

Scholarships and Awards

  • 2024 – HDR scholarship, Edith Cowan University.

Supervisors

Contact

Abid Ishaq
PhD Student
Centre of Artificial Intelligence and Machine Learning
School of Science
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