5 papers
Localising Shortcut Learning in Pixel Space via Ordinal Scoring Correlations for Attribution Representations (OSCAR)
Akshit Achara, Peter Triantafillou, Esther Puyol-Antón +2
Deep neural networks often exploit shortcuts. These are spurious cues which are associated with output labels in the training data but are unrelated to task semantics. When the sho…
Steerable Conditional Diffusion for Domain Adaptation in PET Image Reconstruction
George Webber, Alexander Hammers, Andrew P. King +1
Diffusion models have recently enabled state-of-the-art reconstruction of positron emission tomography (PET) images while requiring only image training data. However, domain shift…
Invisible Attributes, Visible Biases: Exploring Demographic Shortcuts in MRI-based Alzheimer's Disease Classification
Akshit Achara, Esther Puyol Anton, Alexander Hammers +1
Magnetic resonance imaging (MRI) is the gold standard for brain imaging. Deep learning (DL) algorithms have been proposed to aid in the diagnosis of diseases such as Alzheimer's di…
Themed Challenges to Solve Data Scarcity in Africa: A Proposition for Increasing Local Data Collection and Integration
Mubaraq Yakubu, Udunna Anazodo, Maruf Adewole +6
In Africa, the scarcity of computational resources and medical datasets remains a major hurdle to the development and deployment of artificial intelligence (AI) tools in clinical s…
Systematic Review of Pituitary Gland and Pituitary Adenoma Automatic Segmentation Techniques in Magnetic Resonance Imaging
Mubaraq Yakubu, Navodini Wijethilake, Jonathan Shapey +2
Purpose: Accurate segmentation of both the pituitary gland and adenomas from magnetic resonance imaging (MRI) is essential for diagnosis and treatment of pituitary adenomas. This s…