Search Results for author:
Found 21 papers, 15 papers with code
CenTime: Event-Conditional Modelling of Censoring in Survival Analysis
We demonstrate that our approach forms a consistent estimator for the event model parameters, even in the absence of uncensored data.
A data-centric deep learning approach to airway segmentation
In this regard, airway segmentation plays a critical role in the production of the outline of the entire airway tree to enable estimation of disease extent and severity.
A 3D deep learning classifier and its explainability when assessing coronary artery disease
Early detection and diagnosis of coronary artery disease (CAD) could save lives and reduce healthcare costs.
Expectation Maximization Pseudo Labels
In the remainder of the paper, we showcase the applications of pseudo-labelling and its generalised form, Bayesian Pseudo-Labelling, in the semi-supervised segmentation of medical images.
A hybrid CNN-RNN approach for survival analysis in a Lung Cancer Screening study
The Cox neural network can achieve an IPCW C-index of 0. 75 on the internal dataset and 0. 69 on an external dataset.
Airway measurement by refinement of synthetic images improves mortality prediction in idiopathic pulmonary fibrosis
We compare our ATN model with a state-of-the-art GAN-based network (simGAN) using a) qualitative assessment; b) assessment of the ability of ATN and simGAN based CT airway metrics to predict mortality in a population of 113 patients with IPF.
Optimising Chest X-Rays for Image Analysis by Identifying and Removing Confounding Factors
This variation is seen in the CXR projections used, image annotations added and in the inspiratory effort and degree of rotation of clinical images.
Bayesian Pseudo Labels: Expectation Maximization for Robust and Efficient Semi-Supervised Segmentation
Secondly, we propose a semi-supervised medical image segmentation method purely based on the original pseudo labelling, namely SegPL.
Enhancing Cancer Prediction in Challenging Screen-Detected Incident Lung Nodules Using Time-Series Deep Learning
Our model demonstrated comparable and complementary performance to radiologists when interpreting challenging lung nodules and showed improved performance (AUC=88\%) against models utilizing single time-point data only.
Survival Analysis for Idiopathic Pulmonary Fibrosis using CT Images and Incomplete Clinical Data
To this end, we propose a probabilistic model that captures the dependencies between the observed clinical variables and imputes missing ones.
Learning Morphological Feature Perturbations for Calibrated Semi-Supervised Segmentation
We propose MisMatch, a novel consistency-driven semi-supervised segmentation framework which produces predictions that are invariant to learnt feature perturbations.
Evaluation of automated airway morphological quantification for assessing fibrosing lung disease
AirQuant is an open-source pipeline that avoids limitations of existing airway quantification algorithms and has clinical interpretability.
MisMatch: Calibrated Segmentation via Consistency on Differential Morphological Feature Perturbations with Limited Labels
The state-of-the-art SSL methods in image classification utilise consistency regularisation to learn unlabelled predictions which are invariant to input level perturbations.
Learning to Address Intra-segment Misclassification in Retinal Imaging
Accurate multi-class segmentation is a long-standing challenge in medical imaging, especially in scenarios where classes share strong similarity.
Disentangling Human Error from Ground Truth in Segmentation of Medical Images
In all cases, our method outperforms competing methods and relevant baselines particularly in cases where the number of annotations is small and the amount of disagreement is large.
Disentangling Human Error from the Ground Truth in Segmentation of Medical Images
Recent years have seen increasing use of supervised learning methods for segmentation tasks.
Learning To Pay Attention To Mistakes
We compared our methods with state-of-the-art attention mechanisms in medical imaging, including self-attention, spatial-attention and spatial-channel mixed attention.
The challenges of deploying artificial intelligence models in a rapidly evolving pandemic
The COVID-19 pandemic, caused by the severe acute respiratory syndrome coronavirus 2, emerged into a world being rapidly transformed by artificial intelligence (AI) based on big data, computational power and neural networks.
Reproducibility of an airway tapering measurement in CT with application to bronchiectasis
Purpose: This paper proposes a pipeline to acquire a scalar tapering measurement from the carina to the most distal point of an individual airway visible on CT. We show the applicability of using tapering measurements on clinically acquired data by quantifying the reproducibility of the tapering measure.
Tapering Analysis of Airways with Bronchiectasis
We propose a simple measurement of tapering along the airways to diagnose and monitor bronchiectasis.
Modelling Airway Geometry as Stock Market Data using Bayesian Changepoint Detection
We can compute a relative change in airway volume that may be useful for quantifying IPF disease progression.
