11 citations · 22 across the 9 of their papers we have counts for
9 papers
DyABD: The Abdominal Muscle Segmentation in Dynamic MRI Benchmark
Niamh Belton, Victoria Joppin, Aonghus Lawlor +4
This work introduces DyABD, a novel and complex benchmark dataset of dynamic abdominal MRIs from patients with abdominal hernias and associated high quality abdominal muscle annota…
An AI System for Continuous Knee Osteoarthritis Severity Grading Using Self-Supervised Anomaly Detection with Limited Data
Niamh Belton, Aonghus Lawlor, Kathleen M. Curran
The diagnostic accuracy and subjectivity of existing Knee Osteoarthritis (OA) ordinal grading systems has been a subject of on-going debate and concern. Existing automated solution…
Rethinking Knee Osteoarthritis Severity Grading: A Few Shot Self-Supervised Contrastive Learning Approach
Niamh Belton, Misgina Tsighe Hagos, Aonghus Lawlor +1
Knee Osteoarthritis (OA) is a debilitating disease affecting over 250 million people worldwide. Currently, radiologists grade the severity of OA on an ordinal scale from zero to fo…
Distance-Aware eXplanation Based Learning
Misgina Tsighe Hagos, Niamh Belton, Kathleen M. Curran +1
eXplanation Based Learning (XBL) is an interactive learning approach that provides a transparent method of training deep learning models by interacting with their explanations. XBL…
Interpretable Weighted Siamese Network to Predict the Time to Onset of Alzheimer's Disease from MRI Images
Misgina Tsighe Hagos, Niamh Belton, Ronan P. Killeen +2
Alzheimer's Disease (AD) is a progressive disease preceded by Mild Cognitive Impairment (MCI). Early detection of AD is crucial for making treatment decisions. However, most of the…
FewSOME: One-Class Few Shot Anomaly Detection with Siamese Networks
Niamh Belton, Misgina Tsighe Hagos, Aonghus Lawlor +1
Recent Anomaly Detection techniques have progressed the field considerably but at the cost of increasingly complex training pipelines. Such techniques require large amounts of trai…
Optimising Knee Injury Detection with Spatial Attention and Validating Localisation Ability
Niamh Belton, Ivan Welaratne, Adil Dahlan +4
This work employs a pre-trained, multi-view Convolutional Neural Network (CNN) with a spatial attention block to optimise knee injury detection. An open-source Magnetic Resonance I…
Semi-Supervised Siamese Network for Identifying Bad Data in Medical Imaging Datasets
Niamh Belton, Aonghus Lawlor, Kathleen M. Curran
Noisy data present in medical imaging datasets can often aid the development of robust models that are equipped to handle real-world data. However, if the bad data contains insuffi…
A Simplistic Machine Learning Approach to Contact Tracing
Carlos Gómez, Niamh Belton, Boi Quach +2
This report is based on the modified NIST challenge, Too Close For Too Long, provided by the SFI Centre for Machine Learning (ML-Labs). The modified challenge excludes the time cal…