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Chapter and Conference Paper
Boundary-RL: Reinforcement Learning for Weakly-Supervised Prostate Segmentation in TRUS Images
We propose Boundary-RL, a novel weakly supervised segmentation method that utilises only patch-level labels for training. We envision segmentation as a boundary detection problem, rather than a pixel-level cla...
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Article
Open AccessAuthor Correction: Delineating COVID-19 subgroups using routine clinical data identifies distinct in-hospital outcomes
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Article
Open AccessDelineating COVID-19 subgroups using routine clinical data identifies distinct in-hospital outcomes
The COVID-19 pandemic has been a great challenge to healthcare systems worldwide. It highlighted the need for robust predictive models which can be readily deployed to uncover heterogeneities in disease course...
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Article
Open AccessTask model-specific operator skill assessment in routine fetal ultrasound scanning
For highly operator-dependent ultrasound scanning, skill assessment approaches evaluate operator competence given available data, such as acquired images and tracked probe movement. Operator skill level can be...
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Chapter and Conference Paper
Collaborative Quantization Embeddings for Intra-subject Prostate MR Image Registration
Image registration is useful for quantifying morphological changes in longitudinal MR images from prostate cancer patients. This paper describes a development in improving the learning-based registration algor...
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Chapter and Conference Paper
The Impact of Using Voxel-Level Segmentation Metrics on Evaluating Multifocal Prostate Cancer Localisation
Dice similarity coefficient (DSC) and Hausdorff distance (HD) are widely used for evaluating medical image segmentation. They have also been criticised, when reported alone, for their unclear or even misleadin...
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Chapter and Conference Paper
Adaptable Image Quality Assessment Using Meta-Reinforcement Learning of Task Amenability
The performance of many medical image analysis tasks are strongly associated with image data quality. When develo** modern deep learning algorithms, rather than relying on subjective (human-based) image qual...
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Chapter and Conference Paper
Development and Evaluation of Intraoperative Ultrasound Segmentation with Negative Image Frames and Multiple Observer Labels
When develo** deep neural networks for segmenting intraoperative ultrasound images, several practical issues are encountered frequently, such as the presence of ultrasound frames that do not contain regions ...
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Chapter and Conference Paper
Controlling False Positive/Negative Rates for Deep-Learning-Based Prostate Cancer Detection on Multiparametric MR Images
Prostate cancer (PCa) is one of the leading causes of death for men worldwide. Multi-parametric magnetic resonance (mpMR) imaging has emerged as a non-invasive diagnostic tool for detecting and localising pros...
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Chapter and Conference Paper
Learning Image Quality Assessment by Reinforcing Task Amenable Data Selection
In this paper, we consider a type of image quality assessment (IQA) as a task-specific measurement, which can be used to select images that are more amenable to a given target task, such as image classificatio...
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Article
Open AccessMRI Cross-Modality Image-to-Image Translation
We present a cross-modality generation framework that learns to generate translated modalities from given modalities in MR images. Our proposed method performs Image Modality Translation (abbreviated as IMT) b...
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Chapter and Conference Paper
Longitudinal Image Registration with Temporal-Order and Subject-Specificity Discrimination
Morphological analysis of longitudinal MR images plays a key role in monitoring disease progression for prostate cancer patients, who are placed under an active surveillance program. In this paper, we describe...
