Zobrazeno 1 - 10
of 34
pro vyhledávání: '"Hagos, Misgina Tsighe"'
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
Medical image classification models are frequently trained using training datasets derived from multiple data sources. While leveraging multiple data sources is crucial for achieving model generalization, it is important to acknowledge that the diver
Externí odkaz:
http://arxiv.org/abs/2308.01119
eXplanation Based Learning (XBL) is a form of Interactive Machine Learning (IML) that provides a model refining approach via user feedback collected on model explanations. Although the interactivity of XBL promotes model transparency, XBL requires a
Externí odkaz:
http://arxiv.org/abs/2307.06026
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
Identifying spurious correlations learned by a trained model is at the core of refining a trained model and building a trustworthy model. We present a simple method to identify spurious correlations that have been learned by a model trained for image
Externí odkaz:
http://arxiv.org/abs/2211.08285
Explanatory Interactive Learning (XIL) collects user feedback on visual model explanations to implement a Human-in-the-Loop (HITL) based interactive learning scenario. Different user feedback types will have different impacts on user experience and t
Externí odkaz:
http://arxiv.org/abs/2209.12476
Publikováno v:
International Conference on Advances of Science and Technology 2021, LNICST 411, pp 401-411
Poor sitting habits have been identified as a risk factor to musculoskeletal disorders and lower back pain especially on the elderly, disabled people, and office workers. In the current computerized world, even while involved in leisure or work activ
Externí odkaz:
http://arxiv.org/abs/2201.02615
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