ArticleBiomedical optics express2026
Anatomy-guided weakly supervised learning framework for corneal nerve image denoising and enhancement.
Article in Biomedical optics express, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Corneal confocal microscopy (CCM) enables non-invasive imaging of the sub-basal nerve plexus for early diagnosis of diabetic neuropathy, but its utility is hindered by inherent noise and low contrast in raw images. We present NerveBoost, a weakly supervised framework for CCM denoising and enhancement guided by anatomical priors. Unlike supervised methods requiring paired clean data, NerveBoost uses binary nerve masks to construct a pseudo-target via region-specific gamma correction. A composite loss function integrates weighted reconstruction, gradient consistency, background smoothness, and foreground-background contrast constraints to jointly optimize noise suppression and structural enhancement within an encoder-decoder architecture. Results indicate that NerveBoost effectively enhances nerve visibility while maintaining structural fidelity, offering a robust and efficient preprocessing solution for clinical CCM analysis without requiring paired ground-truth data.
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Registered trials
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