7 citations · 19 across the 5 of their papers we have counts for
6 papers
A residual dense vision transformer for medical image super-resolution with segmentation-based perceptual loss fine-tuning
Jin Zhu, Guang Yang, Pietro Lio
Super-resolution plays an essential role in medical imaging because it provides an alternative way to achieve high spatial resolutions and image quality with no extra acquisition c…
Data and Physics Driven Learning Models for Fast MRI -- Fundamentals and Methodologies from CNN, GAN to Attention and Transformers
Jiahao Huang, Yingying Fang, Yang Nan +9
Research studies have shown no qualms about using data driven deep learning models for downstream tasks in medical image analysis, e.g., anatomy segmentation and lesion detection,…
MIASSR: An Approach for Medical Image Arbitrary Scale Super-Resolution
Jin Zhu, Chuan Tan, Junwei Yang +2
Single image super-resolution (SISR) aims to obtain a high-resolution output from one low-resolution image. Currently, deep learning-based SISR approaches have been widely discusse…
Arbitrary Scale Super-Resolution for Brain MRI Images
Chuan Tan, Jin Zhu, Pietro Lio'
Recent attempts at Super-Resolution for medical images used deep learning techniques such as Generative Adversarial Networks (GANs) to achieve perceptually realistic single image S…
How Can We Make GAN Perform Better in Single Medical Image Super-Resolution? A Lesion Focused Multi-Scale Approach
Jin Zhu, Guang Yang, Pietro Lio
Single image super-resolution (SISR) is of great importance as a low-level computer vision task. The fast development of Generative Adversarial Network (GAN) based deep learning ar…
Lesion Focused Super-Resolution
Jin Zhu, Guang Yang, Pietro Lio
Super-resolution (SR) for image enhancement has great importance in medical image applications. Broadly speaking, there are two types of SR, one requires multiple low resolution (L…