51 citations · 178 across the 33 of their papers we have counts for
34 papers
Bayesian adaptively-weighted ensembles for few-shot abdominal segmentation
Abbas Al-Sabbagh, Shalom F. Mushtaq, Tomás M. da Silva +7
Few-shot learning has emerged as a promising approach for anatomical segmentation when labelled data are scarce. However, different few-shot learning algorithms exhibit complementa…
Active few-shot segmentation by reinforcing data selection
Chenlan Zhao, Benny Wong, Timothy F. Lundberg +8
Few-shot learning enables medical image segmentation models to adapt to new tasks using only a small number of labelled examples. However, adaptation performance depends strongly o…
Interpretable Probabilistic Medical Image Segmentation via Gaussian Process with Explicit Modelling of Annotation Bias and Variability
Qi Li, Yuliang Huang, Shaheer U. Saeed +7
Deep learning-based medical image segmentation models are trained using annotations that exhibit systematic bias and variability across raters. While probabilistic multi-rater appr…
Segmentation, Detection and Explanation: A Unified Framework for CT Appearance Reasoning
Yuyuan Liu, Can Peng, Yingyu Yang +3
Recent progress in deep learning has significantly advanced CT image analysis, particularly for segmentation tasks. However, these advances are largely confined to image-level patt…
CT-Guided Spatially-varying Regularization for Voxel-Wise Deformable Whole-Body PET Registration
Xiangcen Wu, Ruohua Chen, Sichun Li +4
Whole-body Positron Emission Tomography (PET) registration is essential for multi-parametric tumor characterization and assessment of metastatic disease progression. In deep learni…
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…
Orientation-Robust Latent Motion Trajectory Learning for Annotation-free Cardiac Phase Detection in Fetal Echocardiography
Yingyu Yang, Qianye Yang, Can Peng +4
Fetal echocardiography is essential for detecting congenital heart disease (CHD), facilitating pregnancy management, optimized delivery planning, and timely postnatal interventions…
Learning to learn skill assessment for fetal ultrasound scanning
Yipei Wang, Qianye Yang, Lior Drukker +3
Traditionally, ultrasound skill assessment has relied on expert supervision and feedback, a process known for its subjectivity and time-intensive nature. Previous works on quantita…
Neural Collapse-Inspired Multi-Label Federated Learning under Label-Distribution Skew
Can Peng, Yuyuan Liu, Yingyu Yang +3
Federated Learning (FL) enables collaborative model training across distributed clients while preserving data privacy, but remains challenging when client data are highly heterogen…
Latent Motion Profiling for Annotation-free Cardiac Phase Detection in Adult and Fetal Echocardiography Videos
Yingyu Yang, Qianye Yang, Kangning Cui +6
The identification of cardiac phase is an essential step for analysis and diagnosis of cardiac function. Automatic methods, especially data-driven methods for cardiac phase detecti…
Asynchronous Federated Continual Segmentation with Evolving Clients and Label Spaces
Can Peng, Qianhui Men, Pramit Saha +5
Federated learning seeks to foster collaboration among distributed clients while preserving the privacy of their local data. Traditional federated learning methods typically assume…
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…
AI-assisted prostate cancer detection and localisation on biparametric MR by classifying radiologist-positives
Xiangcen Wu, Yipei Wang, Qianye Yang +5
Prostate cancer diagnosis through MR imaging have currently relied on radiologists' interpretation, whilst modern AI-based methods have been developed to detect clinically signific…
Nonrigid Reconstruction of Freehand Ultrasound without a Tracker
Qi Li, Ziyi Shen, Qianye Yang +4
Reconstructing 2D freehand Ultrasound (US) frames into 3D space without using a tracker has recently seen advances with deep learning. Predicting good frame-to-frame rigid transfor…
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,…
Boundary-RL: Reinforcement Learning for Weakly-Supervised Prostate Segmentation in TRUS Images
Weixi Yi, Vasilis Stavrinides, Zachary M. C. Baum +5
We propose Boundary-RL, a novel weakly supervised segmentation method that utilises only patch-level labels for training. We envision the segmentation as a boundary detection probl…
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…
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…
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…
Collaborative Quantization Embeddings for Intra-Subject Prostate MR Image Registration
Ziyi Shen, Qianye Yang, Yuming Shen +9
Image registration is useful for quantifying morphological changes in longitudinal MR images from prostate cancer patients. This paper describes a development in improving the lear…
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 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…