Journal articles and conference contributions spanning retinal imaging, oculomics, interpretable machine learning, and systemic health.
Integrates retinal imaging with lipidomic profiles to uncover microvascular and metabolic signatures associated with cardiovascular health.
A broad survey of retinal imaging and oculomics, mapping how ocular biomarkers can inform systemic, cardiovascular, and metabolic health.
Shows how architectural and data-level diversity in CNN ensembles improves robustness under class imbalance and limited medical imaging data.
Examines how ensemble diversity can mitigate data scarcity in diabetic retinopathy modelling and improve generalisation in clinical imaging settings.
Introduces Causal-RetiGraph, a biomedical informatics framework linking interpretable retinal graph phenotypes with NHANES-anchored pathway modelling for diabetic retinopathy.
Introduces a dual-edge spatial-Jacobian image graph framework for interpretable diabetic retinopathy grading by combining vessel-lesion spatial edges with Jacobian-based lesion-biomarker edges.
RetiSEM presents a causal modelling framework for fragmented biomedical data, designed to recover interpretable structure under incomplete, multimodal, and distributed evidence settings.
Explores links between retinal microvascular traits and lipidomic profiles to identify candidate non-invasive biomarkers for cardiovascular risk.
