Enriching AI-based Predictive Models from Retinal Imaging by Multi-Modal Contrastive Pre-training
Sprache des Titels:
Englisch
Original Kurzfassung:
Self-supervised pre-training has demonstrated efficacy in yielding deep learning (DL) models with remarkable data efficiency and generalization capabilities. Retinal imaging has an untapped potential to exploit such approaches by leveraging matched multimodal data in the form of 2D fundus photography/near-infrared reflective imaging (NIR) and 3D spectral domain optical coherence tomography (SD-OCT) scans. We explore multimodal pre-training to enhance DL models on downstream predictive tasks: disease classification, structure-function prediction, and treatment forecasting.
Forschungs-