From the 2 of 5 linked papers with an AI index.
5 papers
Understanding Sources of Demographic Predictability in Brain MRI via Disentangling Anatomy and Contrast
Mehmet Yigit Avci, Akshit Achara, Andrew King +1
The paper introduces a disentangled representation learning framework that separates anatomical structure from acquisition-dependent contrast in brain MRI, showing that demographic…
Metadata Supervised MRI Representations for Modelling and Controlling Acquisition Variability
Mehmet Yigit Avci, Pedro Borges, Virginia Fernandez +5
Magnetic resonance imaging exhibits substantial acquisition variability, where identical anatomy can appear markedly different across scanners and imaging protocols. Consequently,…
DIST-CLIP: Arbitrary Metadata and Image Guided MRI Harmonization via Disentangled Anatomy-Contrast Representations
Mehmet Yigit Avci, Pedro Borges, Virginia Fernandez +4
Deep learning holds immense promise for transforming medical image analysis, yet its clinical generalization remains profoundly limited. A major barrier is data heterogeneity. This…
Metadata-Aligned 3D MRI Representations for Contrast Understanding and Quality Control
Mehmet Yigit Avci, Pedro Borges, Virginia Fernandez +4
Magnetic Resonance Imaging suffers from substantial data heterogeneity and the absence of standardized contrast labels across scanners, protocols, and institutions, which severely…
Neuradicon: operational representation learning of neuroimaging reports
Henry Watkins, Robert Gray, Adam Julius +10
Radiological reports typically summarize the content and interpretation of imaging studies in unstructured form that precludes quantitative analysis. This limits the monitoring of…