8 papers
A continually expandable foundation model for brain MRI
Michail Mamalakis, Carmen Jimenez-Mesa, Yonghao Li +8
Brain magnetic resonance imaging (MRI) is central to neuroscience and clinical assessment, but models are commonly developed for individual diseases, populations or imaging protoco…
A Monosemantic Attribution Framework for Stable Interpretability in Clinical Neuroscience Transformer-Based Language Models
Michail Mamalakis, Tiago Azevedo, Cristian Cosentino +5
Interpretability remains a key challenge for deploying language models (LM) in clinical settings such as progression diagnosis of Alzheimer disease, where early and trustworthy pre…
Geometry-Guided Generative Representation for Functional Brain Graphs
Subati Abulikemu, Tiago Azevedo, Michail Mamalakis +1
In network neuroscience, functional brain systems are often characterized using separate yet related graph-theoretic or spectral descriptors, overlooking how these properties covar…
Association-sensory spatiotemporal hierarchy and functional gradient-regularised recurrent neural network with implications for schizophrenia
Subati Abulikemu, Puria Radmard, Michail Mamalakis +1
The human neocortex is functionally organised at its highest level along a continuous sensory-to-association (AS) hierarchy. This study characterises the AS hierarchy of patients w…
Uncovering Neuroimaging Biomarkers of Brain Tumor Surgery with AI-Driven Methods
Carmen Jimenez-Mesa, Yizhou Wan, Guilio Sansone +7
Brain tumor resection is a highly complex procedure with profound implications for survival and quality of life. Predicting patient outcomes is crucial to guide clinicians in balan…
The Explanation Necessity for Healthcare AI
Michail Mamalakis, Héloïse de Vareilles, Graham Murray +2
Explainability is a critical factor in enhancing the trustworthiness and acceptance of artificial intelligence (AI) in healthcare, where decisions directly impact patient outcomes.…