3 citations · 8 across the 6 of their papers we have counts for
6 papers · 1 filter
SeCo-INR: Semantically Conditioned Implicit Neural Representations for Improved Medical Image Super-Resolution
Mevan Ekanayake, Zhifeng Chen, Gary Egan +2
Implicit Neural Representations (INRs) have recently advanced the field of deep learning due to their ability to learn continuous representations of signals without the need for la…
Motion-Informed Deep Learning for Brain MR Image Reconstruction Framework
Zhifeng Chen, Kamlesh Pawar, Kh Tohidul Islam +3
Motion artifacts in Magnetic Resonance Imaging (MRI) are one of the frequently occurring artifacts due to patient movements during scanning. Motion is estimated to be present in ap…
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…
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…