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pro vyhledávání: '"Belton, Niamh"'
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 solutions are trained to emulate these imperfect systems, whilst also being re
Externí odkaz:
http://arxiv.org/abs/2407.11500
Publikováno v:
Thirty-seventh Conference on Neural Information Processing Systems Workshop on Medical Imaging meets NeurIPS 2023
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 four using the Kellgren-Lawrence (KL) system. Recent studies have raised
Externí odkaz:
http://arxiv.org/abs/2407.09515
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 augments loss functions to penalize a model based on deviation of its
Externí odkaz:
http://arxiv.org/abs/2309.05548
Autor:
Hagos, Misgina Tsighe, Belton, Niamh, Killeen, Ronan P., Curran, Kathleen M., Mac Namee, Brian
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 literature on computer-assisted detection of AD focuses on classifyin
Externí odkaz:
http://arxiv.org/abs/2304.07097
Recent Anomaly Detection techniques have progressed the field considerably but at the cost of increasingly complex training pipelines. Such techniques require large amounts of training data, resulting in computationally expensive algorithms that are
Externí odkaz:
http://arxiv.org/abs/2301.06957
Autor:
Belton, Niamh, Welaratne, Ivan, Dahlan, Adil, Hearne, Ronan T, Hagos, Misgina Tsighe, Lawlor, Aonghus, Curran, Kathleen M.
Publikováno v:
Medical Image Understanding and Analysis (2021) 71-86
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 Imaging (MRI) data set with image-level labels was leveraged for this a
Externí odkaz:
http://arxiv.org/abs/2108.08136
Publikováno v:
Medical Imaging with Deep Learning (2021)
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 insufficient anatomical information, it can have a severe negative effect on
Externí odkaz:
http://arxiv.org/abs/2108.07130
Publikováno v:
In Computerized Medical Imaging and Graphics July 2024 115
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 calculation (too long) aspect. By handcrafting features from phone instru
Externí odkaz:
http://arxiv.org/abs/2012.05940
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