3 citations · 5 across the 6 of their papers we have counts for
6 papers
CADD: Context aware disease deviations via restoration of brain images using normative conditional diffusion models
Ana Lawry Aguila, Ayodeji Ijishakin, Juan Eugenio Iglesias +5
Applying machine learning to real-world medical data, e.g. from hospital archives, has the potential to revolutionize disease detection in brain images. However, detecting patholog…
An X-Ray Is Worth 15 Features: Sparse Autoencoders for Interpretable Radiology Report Generation
Ahmed Abdulaal, Hugo Fry, Nina Montaña-Brown +5
Radiological services are experiencing unprecedented demand, leading to increased interest in automating radiology report generation. Existing Vision-Language Models (VLMs) suffer…
Disentangled Diffusion Autoencoder for Harmonization of Multi-site Neuroimaging Data
Ayodeji Ijishakin, Ana Lawry Aguila, Elizabeth Levitis +3
Combining neuroimaging datasets from multiple sites and scanners can help increase statistical power and thus provide greater insight into subtle neuroanatomical effects. However,…
Normative Diffusion Autoencoders: Application to Amyotrophic Lateral Sclerosis
Ayodeji Ijishakin, Adamos Hadjasavilou, Ahmed Abdulaal +7
Predicting survival in Amyotrophic Lateral Sclerosis (ALS) is a challenging task. Magnetic resonance imaging (MRI) data provide in vivo insight into brain health, but the low preva…
Semi-Supervised Diffusion Model for Brain Age Prediction
Ayodeji Ijishakin, Sophie Martin, Florence Townend +8
Brain age prediction models have succeeded in predicting clinical outcomes in neurodegenerative diseases, but can struggle with tasks involving faster progressing diseases and low…
Interpretable Alzheimer's Disease Classification Via a Contrastive Diffusion Autoencoder
Ayodeji Ijishakin, Ahmed Abdulaal, Adamos Hadjivasiliou +2
In visual object classification, humans often justify their choices by comparing objects to prototypical examples within that class. We may therefore increase the interpretability…