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Email

mevan.ekanayake@unimelb.edu.au

Credentials


Position
Honorary (Fellow)
The Sir Peter MacCallum Department of Oncology
Education
Doctor of Philosophy
Monash University
Bachelor (Honours)
University of Peradeniya
ORCID

0000-0002-4768-5073

Dr Mevan Ekanayake

Honorary (Fellow)
The Sir Peter MacCallum Department of Oncology

18 Scholarly works
0 Projects

HIGHLIGHTS

  • 2026

    Book Chapter

    Ultra-Low-Field MRI Enhancement via INR-Based Style Transfer
    DOI: 10.1007/978-3-032-05325-1_57
  • 2025

    Journal article

    PixCUE: Joint Uncertainty Estimation and Image Reconstruction in MRI using Deep Pixel Classification
    DOI: 10.1007/s10278-024-01250-3
  • 2025

    Journal article

    CL-MRI: Self-Supervised contrastive learning to improve the accuracy of undersampled MRI reconstruction
    DOI: 10.1016/j.bspc.2024.107185
  • 2025

    Conference Proceedings

    SeCo-INR: Semantically Conditioned Implicit Neural Representations for Improved Medical Image Super-Resolution
    DOI: 10.1109/WACV61041.2025.00022
  • 2024

    Journal article

    McSTRA: A multi-branch cascaded swin transformer for point spread function-guided robust MRI reconstruction
    DOI: 10.1016/j.compbiomed.2023.107775
  • 2024

    Conference Proceedings

    Improving Deep Learning MRI Reconstruction with Contrastive Learning Pretraining
    DOI: 10.1109/ISBI56570.2024.10635794
  • 2023

    Conference Proceedings

    COLADA: Contrastive Learning for highly accelerated MR Image Reconstruction
    DOI: 10.58530/2023/0156
Mevan Ekanayake

RECENT SCHOLARLY WORKS

  • 2023

    Journal article

    Deep Learning for Image Enhancement and Correction in Magnetic Resonance Imaging—State-of-the-Art and Challenges
    DOI: 10.1007/s10278-022-00721-9
  • 2021

    Conference Proceedings

    A Comparison Between MPPT Algorithm and DC-DC Convertor Configurations for PV Systems
    DOI: 10.1109/iciafs52090.2021.9605827
  • 2021

    Journal article

    Constrained Nonnegative Matrix Factorization for Blind Hyperspectral Unmixing Incorporating Endmember Independence
    DOI: 10.1109/jstars.2021.3126664

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