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20182023
most citedDuo-SegNet: Adversarial Dual-Views for Semi-Supervised Medical Image Segmentation

3 citations · 8 across the 4 of their papers we have counts for

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6 papers · 1 filter

eess.IV2023

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…

eess.IV20231 cited

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…

eess.IV20222 cited

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…

eess.IV20223 cited

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…

eess.IV2021

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…

eess.IV2018

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…