19 citations · 42 across the 18 of their papers we have counts for
18 papers
A 3D deep learning classifier and its explainability when assessing coronary artery disease
Wing Keung Cheung, Jeremy Kalindjian, Robert Bell +11
Early detection and diagnosis of coronary artery disease (CAD) could save lives and reduce healthcare costs. The current clinical practice is to perform CAD diagnosis through analy…
Interpolation-Split: a data-centric deep learning approach with big interpolated data to boost airway segmentation performance
Wing Keung Cheung, Ashkan Pakzad, Nesrin Mogulkoc +14
The morphology and distribution of airway tree abnormalities enables diagnosis and disease characterisation across a variety of chronic respiratory conditions. In this regard, airw…
CAMIL: Context-Aware Multiple Instance Learning for Cancer Detection and Subtyping in Whole Slide Images
Olga Fourkioti, Matt De Vries, Chen Jin +2
The visual examination of tissue biopsy sections is fundamental for cancer diagnosis, with pathologists analyzing sections at multiple magnifications to discern tumor cells and the…
Expectation Maximization Pseudo Labels
Moucheng Xu, Yukun Zhou, Chen Jin +5
In this paper, we study pseudo-labelling. Pseudo-labelling employs raw inferences on unlabelled data as pseudo-labels for self-training. We elucidate the empirical successes of pse…
A hybrid CNN-RNN approach for survival analysis in a Lung Cancer Screening study
Yaozhi Lu, Shahab Aslani, An Zhao +5
In this study, we present a hybrid CNN-RNN approach to investigate long-term survival of subjects in a lung cancer screening study. Subjects who died of cardiovascular and respirat…
DeepBrainPrint: A Novel Contrastive Framework for Brain MRI Re-Identification
Lemuel Puglisi, Frederik Barkhof, Daniel C. Alexander +3
Recent advances in MRI have led to the creation of large datasets. With the increase in data volume, it has become difficult to locate previous scans of the same patient within the…
Deformably-Scaled Transposed Convolution
Stefano B. Blumberg, Daniele Raví, Mou-Cheng Xu +3
Transposed convolution is crucial for generating high-resolution outputs, yet has received little attention compared to convolution layers. In this work we revisit transposed convo…
Experimental Design for Multi-Channel Imaging via Task-Driven Feature Selection
Stefano B. Blumberg, Paddy J. Slator, Daniel C. Alexander
This paper presents a data-driven, task-specific paradigm for experimental design, to shorten acquisition time, reduce costs, and accelerate the deployment of imaging devices. Curr…
Bayesian Pseudo Labels: Expectation Maximization for Robust and Efficient Semi-Supervised Segmentation
Mou-Cheng Xu, Yukun Zhou, Chen Jin +5
This paper concerns pseudo labelling in segmentation. Our contribution is fourfold. Firstly, we present a new formulation of pseudo-labelling as an Expectation-Maximization (EM) al…
Optimising Chest X-Rays for Image Analysis by Identifying and Removing Confounding Factors
Shahab Aslani, Watjana Lilaonitkul, Vaishnavi Gnanananthan +7
During the COVID-19 pandemic, the sheer volume of imaging performed in an emergency setting for COVID-19 diagnosis has resulted in a wide variability of clinical CXR acquisitions.…
An efficient semi-supervised quality control system trained using physics-based MRI-artefact generators and adversarial training
Daniele Ravi, Frederik Barkhof, Daniel C. Alexander +3
Large medical imaging data sets are becoming increasingly available, but ensuring sample quality without significant artefacts is challenging. Existing methods for identifying impe…
Learning Morphological Feature Perturbations for Calibrated Semi-Supervised Segmentation
Mou-Cheng Xu, Yu-Kun Zhou, Chen Jin +6
We propose MisMatch, a novel consistency-driven semi-supervised segmentation framework which produces predictions that are invariant to learnt feature perturbations. MisMatch consi…
Enhancing Cancer Prediction in Challenging Screen-Detected Incident Lung Nodules Using Time-Series Deep Learning
Shahab Aslani, Pavan Alluri, Eyjolfur Gudmundsson +10
Lung cancer is the leading cause of cancer-related mortality worldwide. Lung cancer screening (LCS) using annual low-dose computed tomography (CT) scanning has been proven to signi…
Survival Analysis for Idiopathic Pulmonary Fibrosis using CT Images and Incomplete Clinical Data
Ahmed H. Shahin, Joseph Jacob, Daniel C. Alexander +1
Idiopathic Pulmonary Fibrosis (IPF) is an inexorably progressive fibrotic lung disease with a variable and unpredictable rate of progression. CT scans of the lungs inform clinical…
VAFO-Loss: VAscular Feature Optimised Loss Function for Retinal Artery/Vein Segmentation
Yukun Zhou, Moucheng Xu, Yipeng Hu +5
Estimating clinically-relevant vascular features following vessel segmentation is a standard pipeline for retinal vessel analysis, which provides potential ocular biomarkers for bo…
Progressive Subsampling for Oversampled Data - Application to Quantitative MRI
Stefano B. Blumberg, Hongxiang Lin, Francesco Grussu +3
We present PROSUB: PROgressive SUBsampling, a deep learning based, automated methodology that subsamples an oversampled data set (e.g. multi-channeled 3D images) with minimal loss…
Ten years of image analysis and machine learning competitions in dementia
Esther E. Bron, Stefan Klein, Annika Reinke +4
Machine learning methods exploiting multi-parametric biomarkers, especially based on neuroimaging, have huge potential to improve early diagnosis of dementia and to predict which i…
MisMatch: Calibrated Segmentation via Consistency on Differential Morphological Feature Perturbations with Limited Labels
Mou-Cheng Xu, Yukun Zhou, Chen Jin +4
Semi-supervised learning (SSL) is a promising machine learning paradigm to address the issue of label scarcity in medical imaging. SSL methods were originally developed in image cl…