80 citations · 134 across the 15 of their papers we have counts for
15 papers
MRI Parameter Mapping via Gaussian Mixture VAE: Breaking the Assumption of Independent Pixels
Moucheng Xu, Yukun Zhou, Tobias Goodwin-Allcock +4
We introduce and demonstrate a new paradigm for quantitative parameter mapping in MRI. Parameter mapping techniques, such as diffusion MRI and quantitative MRI, have the potential…
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…
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…
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…
Airway measurement by refinement of synthetic images improves mortality prediction in idiopathic pulmonary fibrosis
Ashkan Pakzad, Mou-Cheng Xu, Wing Keung Cheung +7
Several chronic lung diseases, like idiopathic pulmonary fibrosis (IPF) are characterised by abnormal dilatation of the airways. Quantification of airway features on computed tomog…
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.…
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…
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…
Learning to Address Intra-segment Misclassification in Retinal Imaging
Yukun Zhou, Moucheng Xu, Yipeng Hu +4
Accurate multi-class segmentation is a long-standing challenge in medical imaging, especially in scenarios where classes share strong similarity. Segmenting retinal blood vessels i…
Learning To Pay Attention To Mistakes
Mou-Cheng Xu, Neil P. Oxtoby, Daniel C. Alexander +1
In convolutional neural network based medical image segmentation, the periphery of foreground regions representing malignant tissues may be disproportionately assigned as belonging…
Disentangling Human Error from the Ground Truth in Segmentation of Medical Images
Le Zhang, Ryutaro Tanno, Mou-Cheng Xu +5
Recent years have seen increasing use of supervised learning methods for segmentation tasks. However, the predictive performance of these algorithms depends on the quality of label…