Abstract: This article introduces a simple framework for depth-augmented contrastive learning (SimDCL), a novel approach to enhance endoscopic image classification by incorporating depth information.
Advanced statistical modelling, hypothesis testing, and academic workflows make R preferred for data-heavy research and reproducible ...
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