38 citations · 83 across the 23 of their papers we have counts for
24 papers
Contrastive uncertainty learning for robust multi-structure segmentation in lower-pelvic MR
Bilal Sidiqi, Yipei Wang, Wen Yan +3
Boundary delineation of anatomical structures in prostate MRI is often compromised by regional ambiguity. Automated segmentation methods relying on global overlap loss functions te…
Causal-Adversarial Probing of Clinical Covariates for Prostate MRI Grading
Yipei Wang, Shiqi Huang, Wen Yan +6
Deep learning models for prostate MRI-based cancer grading may encode clinical covariates that either reflect useful disease-related signal or non-generalising shortcut information…
Maximizing T2-Only Prostate Cancer Localization from Expected Diffusion Weighted Imaging
Weixi Yi, Yipei Wang, Wen Yan +10
Multiparametric MRI is increasingly recommended as a first-line noninvasive approach to detect and localize prostate cancer, requiring at minimum diffusion-weighted (DWI) and T2-we…
On the Degrees of Freedom of Gridded Control Points in Learning-Based Medical Image Registration
Wen Yan, Qianye Yang, Yipei Wang +5
Many registration problems are ill-posed in homogeneous or noisy regions, and dense voxel-wise decoders can be unnecessarily high-dimensional. A sparse control-point parameterisati…
ProFound: A moderate-sized vision foundation model for multi-task prostate imaging
Yipei Wang, Yinsong Xu, Weixi Yi +11
Many diagnostic and therapeutic clinical tasks for prostate cancer increasingly rely on multi-parametric MRI. Automating these tasks is challenging because they necessitate expert…
Flow Matching-enabled Test-Time Refinement for Unsupervised Cardiac MR Registration
Yunguan Fu, Wenjia Bai, Wen Yan +3
Diffusion-based unsupervised image registration has been explored for cardiac cine MR, but expensive multi-step inference limits practical use. We propose FlowReg, a flow-matching…
Deep EM with Hierarchical Latent Label Modelling for Multi-Site Prostate Lesion Segmentation
Wen Yan, Yipei Wang, Shiqi Huang +5
Label variability is a major challenge for prostate lesion segmentation. In multi-site datasets, annotations often reflect centre-specific contouring protocols, causing segmentatio…
Impact of Clinical Image Quality on Efficient Foundation Model Finetuning
Yucheng Tang, Pawel Rajwa, Alexander Ng +11
Foundation models in medical imaging have shown promising label efficiency, achieving high performance on downstream tasks using only a fraction of the annotated data otherwise req…
Register Anything: Estimating "Corresponding Prompts" for Segment Anything Model
Shiqi Huang, Tingfa Xu, Wen Yan +2
Establishing pixel/voxel-level or region-level correspondences is the core challenge in image registration. The latter, also known as region-based correspondence representation, le…
Tell2Reg: Establishing spatial correspondence between images by the same language prompts
Wen Yan, Qianye Yang, Shiqi Huang +6
Spatial correspondence can be represented by pairs of segmented regions, such that the image registration networks aim to segment corresponding regions rather than predicting displ…
SAMReg: SAM-enabled Image Registration with ROI-based Correspondence
Shiqi Huang, Tingfa Xu, Ziyi Shen +4
This paper describes a new spatial correspondence representation based on paired regions-of-interest (ROIs), for medical image registration. The distinct properties of the proposed…
One registration is worth two segmentations
Shiqi Huang, Tingfa Xu, Ziyi Shen +4
The goal of image registration is to establish spatial correspondence between two or more images, traditionally through dense displacement fields (DDFs) or parametric transformatio…
Weakly supervised localisation of prostate cancer using reinforcement learning for bi-parametric MR images
Martynas Pocius, Wen Yan, Dean C. Barratt +4
In this paper we propose a reinforcement learning based weakly supervised system for localisation. We train a controller function to localise regions of interest within an image by…
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,…
Combiner and HyperCombiner Networks: Rules to Combine Multimodality MR Images for Prostate Cancer Localisation
Wen Yan, Bernard Chiu, Ziyi Shen +8
One of the distinct characteristics in radiologists' reading of multiparametric prostate MR scans, using reporting systems such as PI-RADS v2.1, is to score individual types of MR…
Machine Learning Force Fields with Data Cost Aware Training
Alexander Bukharin, Tianyi Liu, Shengjie Wang +4
Machine learning force fields (MLFF) have been proposed to accelerate molecular dynamics (MD) simulation, which finds widespread applications in chemistry and biomedical research.…
Bi-parametric prostate MR image synthesis using pathology and sequence-conditioned stable diffusion
Shaheer U. Saeed, Tom Syer, Wen Yan +6
We propose an image synthesis mechanism for multi-sequence prostate MR images conditioned on text, to control lesion presence and sequence, as well as to generate paired bi-paramet…
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,…
Cross-Modality Image Registration using a Training-Time Privileged Third Modality
Qianye Yang, David Atkinson, Yunguan Fu +7
In this work, we consider the task of pairwise cross-modality image registration, which may benefit from exploiting additional images available only at training time from an additi…
The impact of using voxel-level segmentation metrics on evaluating multifocal prostate cancer localisation
Wen Yan, Qianye Yang, Tom Syer +6
Dice similarity coefficient (DSC) and Hausdorff distance (HD) are widely used for evaluating medical image segmentation. They have also been criticised, when reported alone, for th…
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…
Controlling False Positive/Negative Rates for Deep-Learning-Based Prostate Cancer Detection on Multiparametric MR images
Zhe Min, Fernando J. Bianco, Qianye Yang +4
Prostate cancer (PCa) is one of the leading causes of death for men worldwide. Multi-parametric magnetic resonance (mpMR) imaging has emerged as a non-invasive diagnostic tool for…
Unsupervised End-to-end Learning for Deformable Medical Image Registration
Siyuan Shan, Wen Yan, Xiaoqing Guo +3
We propose a registration algorithm for 2D CT/MRI medical images with a new unsupervised end-to-end strategy using convolutional neural networks. The contributions of our algorithm…