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Chapter and Conference Paper
CXR-FL: Deep Learning-Based Chest X-ray Image Analysis Using Federated Learning
Federated learning enables building a shared model from multicentre data while storing the training data locally for privacy. In this paper, we present an evaluation (called CXR-FL) of deep learning-based mode...
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Chapter and Conference Paper
Catching Patient’s Attention at the Right Time to Help Them Undergo Behavioural Change: Stress Classification Experiment from Blood Volume Pulse
The CAPABLE project aims to improve the wellbeing of cancer patients managed at home via a coaching system recommending personalized evidence-based health behavioral change interventions and supporting patient...
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Chapter and Conference Paper
Continual Class Incremental Learning for CT Thoracic Segmentation
Deep learning organ segmentation approaches require large amounts of annotated training data, which is limited in supply due to reasons of confidentiality and the time required for expert manual annotation. Th...
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Chapter and Conference Paper
Paying Per-Label Attention for Multi-label Extraction from Radiology Reports
Training medical image analysis models requires large amounts of expertly annotated data which is time-consuming and expensive to obtain. Images are often accompanied by free-text radiology reports which are a...
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Chapter and Conference Paper
Comparison of Active Learning Strategies Applied to Lung Nodule Segmentation in CT Scans
Supervised machine learning techniques require large amounts of annotated training data to attain good performance. Active learning aims to ease the data collection process by automatically detecting which ins...
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Chapter and Conference Paper
Evaluation of Dense Vessel Detection in NCCT Scans
Automatic detection and measurement of dense vessels may enhance the clinical workflow for treatment triage in acute ischemic stroke. In this paper we use a 3D Convolutional Neural Network, which incorporates ...
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Chapter and Conference Paper
Context-Aware Convolutional Neural Networks for Stroke Sign Detection in Non-contrast CT Scans
Detection of acute stroke signs in non-contrast CT images is a challenging task. The intensity and texture variations in pathological regions are subtle and can be confounded by normal physiological changes or...
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Chapter and Conference Paper
Evaluation of an Automatic ASPECT Scoring System for Acute Stroke in Non-Contrast CT
Determining the severity of ischemic stroke in non-contrast CT is a difficult problem due to a low signal to noise ratio. This leads to variable interpretation of ischemic stroke severity. We investigate the l...
