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20182025
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eess.IV2025

An Adversarial Approach to Register Extreme Resolution Tissue Cleared 3D Brain Images

Abdullah Naziba, Clinton Fookes, Dimitri Perrin

We developed a generative patch based 3D image registration model that can register very high resolution images obtained from a biochemical process name tissue clearing. Tissue cle…

eess.IV20241 cited

Size and Smoothness Aware Adaptive Focal Loss for Small Tumor Segmentation

Md Rakibul Islam, Riad Hassan, Abdullah Nazib +3

Deep learning has achieved remarkable accuracy in medical image segmentation, particularly for larger structures with well-defined boundaries. However, its effectiveness can be cha…

eess.IV2023

Uncertainty Driven Bottleneck Attention U-net for Organ at Risk Segmentation

Abdullah Nazib, Riad Hassan, Zahidul Islam +1

Organ at risk (OAR) segmentation in computed tomography (CT) imagery is a difficult task for automated segmentation methods and can be crucial for downstream radiation treatment pl…

eess.IV2020

A Multiple Decoder CNN for Inverse Consistent 3D Image Registration

Abdullah Nazib, Clinton Fookes, Olivier Salvado +1

The recent application of deep learning technologies in medical image registration has exponentially decreased the registration time and gradually increased registration accuracy w…

eess.IV2019

Dense Deformation Network for High Resolution Tissue Cleared Image Registration

Abdullah Nazib, Clinton Fookes, Dimitri Perrin

The recent application of deep learning in various areas of medical image analysis has brought excellent performance gains. In particular, technologies based on deep learning in me…