70 citations · 90 across the 9 of their papers we have counts for
9 papers
CL-MRI: Self-Supervised Contrastive Learning to Improve the Accuracy of Undersampled MRI Reconstruction
Mevan Ekanayake, Zhifeng Chen, Mehrtash Harandi +2
In Magnetic Resonance Imaging (MRI), image acquisitions are often undersampled in the measurement domain to accelerate the scanning process, at the expense of image quality. Howeve…
PixCUE: Joint Uncertainty Estimation and Image Reconstruction in MRI using Deep Pixel Classification
Mevan Ekanayake, Kamlesh Pawar, Gary Egan +1
Deep learning (DL) models are capable of successfully exploiting latent representations in MR data and have become state-of-the-art for accelerated MRI reconstruction. However, und…
Hybrid Window Attention Based Transformer Architecture for Brain Tumor Segmentation
Himashi Peiris, Munawar Hayat, Zhaolin Chen +2
As intensities of MRI volumes are inconsistent across institutes, it is essential to extract universal features of multi-modal MRIs to precisely segment brain tumors. In this conce…
Multi-branch Cascaded Swin Transformers with Attention to k-space Sampling Pattern for Accelerated MRI Reconstruction
Mevan Ekanayake, Kamlesh Pawar, Mehrtash Harandi +2
Global correlations are widely seen in human anatomical structures due to similarity across tissues and bones. These correlations are reflected in magnetic resonance imaging (MRI)…
Reciprocal Adversarial Learning for Brain Tumor Segmentation: A Solution to BraTS Challenge 2021 Segmentation Task
Himashi Peiris, Zhaolin Chen, Gary Egan +1
This paper proposes an adversarial learning based training approach for brain tumor segmentation task. In this concept, the 3D segmentation network learns from dual reciprocal adve…
A Robust Volumetric Transformer for Accurate 3D Tumor Segmentation
Himashi Peiris, Munawar Hayat, Zhaolin Chen +2
We propose a Transformer architecture for volumetric segmentation, a challenging task that requires keeping a complex balance in encoding local and global spatial cues, and preserv…
Duo-SegNet: Adversarial Dual-Views for Semi-Supervised Medical Image Segmentation
Himashi Peiris, Zhaolin Chen, Gary Egan +1
Segmentation of images is a long-standing challenge in medical AI. This is mainly due to the fact that training a neural network to perform image segmentation requires a significan…
Towards Lower-Dose PET using Physics-Based Uncertainty-Aware Multimodal Learning with Robustness to Out-of-Distribution Data
Viswanath P. Sudarshan, Uddeshya Upadhyay, Gary F. Egan +2
Radiation exposure in positron emission tomography (PET) imaging limits its usage in the studies of radiation-sensitive populations, e.g., pregnant women, children, and adults that…
MoCoNet: Motion Correction in 3D MPRAGE images using a Convolutional Neural Network approach
Kamlesh Pawar, Zhaolin Chen, N. Jon Shah +1
Purpose: The suppression of motion artefacts from MR images is a challenging task. The purpose of this paper is to develop a standalone novel technique to suppress motion artefacts…