1

MisMatch: Calibrated Segmentation via Consistency on Differential Morphological Feature Perturbations with Limited Labels
Semi-supervised learning (SSL) is a promising machine learning paradigm to address the ubiquitous issue of label scarcity in medical …
PDF Code
Airway measurement by refinement of synthetic images improves mortality prediction in idiopathic pulmonary fibrosis
Several chronic lung diseases, like idiopathic pulmonary fibrosis (IPF) are characterised by abnormal dilatation of the airways. …
PDF Cite Code DOI
Airway measurement by refinement of synthetic images improves mortality prediction in idiopathic pulmonary fibrosis
Bayesian Pseudo Labels: Expectation Maximization for Robust and Efficient Semi Supervised Segmentation
This paper concerns pseudo labelling in segmentation. Our contribution is fourfold. Firstly, we present a new formulation of …
PDF Cite Code DOI
Survival Analysis for Idiopathic Pulmonary Fibrosis using CT Images and Incomplete Clinical Data
Idiopathic Pulmonary Fibrosis (IPF) is an inexorably progressive fibrotic lung disease with a variable and unpredictable rate of …
PDF Cite Code
Survival Analysis for Idiopathic Pulmonary Fibrosis using CT Images and Incomplete Clinical Data
Learning Morphological Feature Perturbation for Semi-Supervised Segmentation
We propose MisMatch, a novel consistency-driven semi-supervised segmentation framework which produces predictions that are invariant to …
PDF Cite Code
Learning Morphological Feature Perturbation for Semi-Supervised Segmentation
Disentangling Human Error from Ground Truth in Segmentation of Medical Images
Recent years have seen increasing use of supervised learning methods for segmentation tasks. However, the predictive performance of …
PDF Cite Code
Disentangling Human Error from Ground Truth in Segmentation of Medical Images
Learning to Pay Attention to Mistakes
In convolutional neural network based medical image segmentation, the periphery of foreground regions representing malignant tissues …
PDF Cite Code
Learning to Pay Attention to Mistakes