452 citations · 653 across the 22 of their papers we have counts for
13 papers · 1 filter
A versatile foundation model for cine cardiac magnetic resonance image analysis tasks
Yunguan Fu, Wenjia Bai, Weixi Yi +7
Here we present a versatile foundation model that can perform a range of clinically-relevant image analysis tasks, including segmentation, landmark localisation, diagnosis, and pro…
Semi-weakly-supervised neural network training for medical image registration
Yiwen Li, Yunguan Fu, Iani J. M. B. Gayo +11
For training registration networks, weak supervision from segmented corresponding regions-of-interest (ROIs) have been proven effective for (a) supplementing unsupervised methods,…
A Recycling Training Strategy for Medical Image Segmentation with Diffusion Denoising Models
Yunguan Fu, Yiwen Li, Shaheer U Saeed +2
Denoising diffusion models have found applications in image segmentation by generating segmented masks conditioned on images. Existing studies predominantly focus on adjusting mode…
Spatial Correspondence between Graph Neural Network-Segmented Images
Qian Li, Yunguan Fu, Qianye Yang +3
Graph neural networks (GNNs) have been proposed for medical image segmentation, by predicting anatomical structures represented by graphs of vertices and edges. One such type of gr…
Importance of Aligning Training Strategy with Evaluation for Diffusion Models in 3D Multiclass Segmentation
Yunguan Fu, Yiwen Li, Shaheer U. Saeed +2
Recently, denoising diffusion probabilistic models (DDPM) have been applied to image segmentation by generating segmentation masks conditioned on images, while the applications wer…
Prototypical few-shot segmentation for cross-institution male pelvic structures with spatial registration
Yiwen Li, Yunguan Fu, Iani Gayo +12
The prowess that makes few-shot learning desirable in medical image analysis is the efficient use of the support image data, which are labelled to classify or segment new classes,…
Image quality assessment for machine learning tasks using meta-reinforcement learning
Shaheer U. Saeed, Yunguan Fu, Vasilis Stavrinides +8
In this paper, we consider image quality assessment (IQA) as a measure of how images are amenable with respect to a given downstream task, or task amenability. When the task is per…
Image quality assessment by overlapping task-specific and task-agnostic measures: application to prostate multiparametric MR images for cancer segmentation
Shaheer U. Saeed, Wen Yan, Yunguan Fu +9
Image quality assessment (IQA) in medical imaging can be used to ensure that downstream clinical tasks can be reliably performed. Quantifying the impact of an image on the specific…
Few-shot image segmentation for cross-institution male pelvic organs using registration-assisted prototypical learning
Yiwen Li, Yunguan Fu, Qianye Yang +6
The ability to adapt medical image segmentation networks for a novel class such as an unseen anatomical or pathological structure, when only a few labelled examples of this class a…
Morphological Change Forecasting for Prostate Glands using Feature-based Registration and Kernel Density Extrapolation
Qianye Yang, Tom Vercauteren, Yunguan Fu +7
Organ morphology is a key indicator for prostate disease diagnosis and prognosis. For instance, In longitudinal study of prostate cancer patients under active surveillance, the vol…
DeepReg: a deep learning toolkit for medical image registration
Yunguan Fu, Nina Montaña Brown, Shaheer U. Saeed +14
DeepReg (https://github.com/DeepRegNet/DeepReg) is a community-supported open-source toolkit for research and education in medical image registration using deep learning.
Longitudinal Image Registration with Temporal-order and Subject-specificity Discrimination
Qianye Yang, Yunguan Fu, Francesco Giganti +6
Morphological analysis of longitudinal MR images plays a key role in monitoring disease progression for prostate cancer patients, who are placed under an active surveillance progra…
More unlabelled data or label more data? A study on semi-supervised laparoscopic image segmentation
Yunguan Fu, Maria R. Robu, Bongjin Koo +6
Improving a semi-supervised image segmentation task has the option of adding more unlabelled images, labelling the unlabelled images or combining both, as neither image acquisition…