78 citations · 290 across the 25 of their papers we have counts for
28 papers
IIHT: Medical Report Generation with Image-to-Indicator Hierarchical Transformer
Keqiang Fan, Xiaohao Cai, Mahesan Niranjan
Automated medical report generation has become increasingly important in medical analysis. It can produce computer-aided diagnosis descriptions and thus significantly alleviate the…
A Randomized Block Krylov Method for Tensor Train Approximation
Gaohang Yu, Jinhong Feng, Zhongming Chen +2
Tensor train decomposition is a powerful tool for dealing with high-dimensional, large-scale tensor data, which is not suffering from the curse of dimensionality. To accelerate the…
Proximal nested sampling with data-driven priors for physical scientists
Jason D. McEwen, Tobías I. Liaudat, Matthew A. Price +2
Proximal nested sampling was introduced recently to open up Bayesian model selection for high-dimensional problems such as computational imaging. The framework is suitable for mode…
A Bilevel Formalism for the Peer-Reviewing Problem
Gennaro Auricchio, Ruixiao Zhang, Jie Zhang +1
Due to the large number of submissions that more and more conferences experience, finding an automatized way to well distribute the submitted papers among reviewers has become nece…
Few-shot Learning for Inference in Medical Imaging with Subspace Feature Representations
Jiahui Liu, Keqiang Fan, Xiaohao Cai +1
Unlike the field of visual scene recognition where tremendous advances have taken place due to the availability of very large datasets to train deep neural networks, inference from…
GO-LDA: Generalised Optimal Linear Discriminant Analysis
Jiahui Liu, Xiaohao Cai, Mahesan Niranjan
Linear discriminant analysis (LDA) has been a useful tool in pattern recognition and data analysis research and practice. While linearity of class boundaries cannot always be expec…
Semantic Segmentation by Semantic Proportions
Halil Ibrahim Aysel, Xiaohao Cai, Adam Prügel-Bennett
Semantic segmentation is a critical task in computer vision aiming to identify and classify individual pixels in an image, with numerous applications in for example autonomous driv…
Practical Sketching Algorithms for Low-Rank Tucker Approximation of Large Tensors
Wandi Dong, Gaohang Yu, Liqun Qi +1
Low-rank approximation of tensors has been widely used in high-dimensional data analysis. It usually involves singular value decomposition (SVD) of large-scale matrices with high c…
Proximal nested sampling for high-dimensional Bayesian model selection
Xiaohao Cai, Jason D. McEwen, Marcelo Pereyra
Bayesian model selection provides a powerful framework for objectively comparing models directly from observed data, without reference to ground truth data. However, Bayesian model…
3D Orientation Field Transform
Wai-Tsun Yeung, Xiaohao Cai, Zizhen Liang +1
The two-dimensional (2D) orientation field transform has been proved to be effective at enhancing 2D contours and curves in images by means of top-down processing. It, however, has…
Offline and online reconstruction for radio interferometric imaging
Xiaohao Cai, Luke Pratley, Jason D. McEwen
Radio astronomy is transitioning to a big-data era due to the emerging generation of radio interferometric (RI) telescopes, such as the Square Kilometre Array (SKA), which will acq…
A Two-stage Classification Method for High-dimensional Data and Point Clouds
Xiaohao Cai, Raymond Chan, Xiaoyu Xie +1
High-dimensional data classification is a fundamental task in machine learning and imaging science. In this paper, we propose a two-stage multiphase semi-supervised classification…
Three-dimensional Segmentation of Trees Through a Flexible Multi-Class Graph Cut Algorithm (MCGC)
Jonathan Williams, Carola-Bibiane Schönlieb, Tom Swinfield +4
Developing a robust algorithm for automatic individual tree crown (ITC) detection from laser scanning datasets is important for tracking the responses of trees to anthropogenic cha…
Distributed and parallel sparse convex optimization for radio interferometry with PURIFY
Luke Pratley, Jason D. McEwen, Mayeul d'Avezac +4
Next generation radio interferometric telescopes are entering an era of big data with extremely large data sets. While these telescopes can observe the sky in higher sensitivity an…
Sparse Bayesian mass-mapping with uncertainties: peak statistics and feature locations
Matthew A. Price, Xiaohao Cai, Jason D. McEwen +1
Weak lensing convergence maps - upon which higher order statistics can be calculated - can be recovered from observations of the shear field by solving the lensing inverse problem.…
Sparse Bayesian mass-mapping with uncertainties: local credible intervals
Matthew A. Price, Xiaohao Cai, Jason D. McEwen +2
Until recently mass-mapping techniques for weak gravitational lensing convergence reconstruction have lacked a principled statistical framework upon which to quantify reconstructio…
Sparse Bayesian mass-mapping with uncertainties: hypothesis testing of structure
Matthew A. Price, Jason D. McEwen, Xiaohao Cai +2
A crucial aspect of mass-mapping, via weak lensing, is quantification of the uncertainty introduced during the reconstruction process. Properly accounting for these errors has been…
Quantifying Uncertainty in High Dimensional Inverse Problems by Convex Optimisation
Xiaohao Cai, Marcelo Pereyra, Jason D. McEwen
Inverse problems play a key role in modern image/signal processing methods. However, since they are generally ill-conditioned or ill-posed due to lack of observations, their soluti…
Linkage between piecewise constant Mumford-Shah model and ROF model and its virtue in image segmentation
Xiaohao Cai, Raymond Chan, Carola-Bibiane Schonlieb +2
The piecewise constant Mumford-Shah (PCMS) model and the Rudin-Osher-Fatemi (ROF) model are two important variational models in image segmentation and image restoration, respective…
Online radio interferometric imaging: assimilating and discarding visibilities on arrival
Xiaohao Cai, Luke Pratley, Jason D. McEwen
The emerging generation of radio interferometric (RI) telescopes, such as the Square Kilometre Array (SKA), will acquire massive volumes of data and transition radio astronomy to a…
Uncertainty quantification for radio interferometric imaging: II. MAP estimation
Xiaohao Cai, Marcelo Pereyra, Jason D. McEwen
Uncertainty quantification is a critical missing component in radio interferometric imaging that will only become increasingly important as the big-data era of radio interferometry…
Uncertainty quantification for radio interferometric imaging: I. proximal MCMC methods
Xiaohao Cai, Marcelo Pereyra, Jason D. McEwen
Uncertainty quantification is a critical missing component in radio interferometric imaging that will only become increasingly important as the big-data era of radio interferometry…
A graph cut approach to 3D tree delineation, using integrated airborne LiDAR and hyperspectral imagery
Juheon Lee, David Coomes, Carola-Bibiane Schonlieb +6
Recognising individual trees within remotely sensed imagery has important applications in forest ecology and management. Several algorithms for tree delineation have been suggested…
Wavelet-Based Segmentation on the Sphere
Xiaohao Cai, Christopher G. R. Wallis, Jennifer Y. H. Chan +1
Segmentation, a useful/powerful technique in pattern recognition, is the process of identifying object outlines within images. There are a number of efficient algorithms for segmen…