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Metadata Supervised Imaging Representations for Modelling and Controlling Acquisition Variability
Mehmet Yigit Avci, Pedro Borges, Virginia Fernandez +5
Biomedical imaging data exhibit substantial acquisition variability, where identical biological structures can appear markedly different due to differences in imaging devices, acqu…
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…
MR-CLIP: Efficient Metadata-Guided Learning of MRI Contrast Representations
Mehmet Yigit Avci, Pedro Borges, Paul Wright +3
Accurate interpretation of Magnetic Resonance Imaging scans in clinical systems is based on a precise understanding of image contrast. This contrast is primarily governed by acquis…
Deep Attention Based Semi-Supervised 2D-Pose Estimation for Surgical Instruments
Mert Kayhan, Okan Köpüklü, Mhd Hasan Sarhan +3
For many practical problems and applications, it is not feasible to create a vast and accurately labeled dataset, which restricts the application of deep learning in many areas. Se…