Rethinking Glaucoma Calibration: Voting-Based Binocular and Metadata Integration
This addresses the need for reliable predictions in glaucoma diagnosis to prevent overdiagnosis or missed cases, though it appears incremental as it builds on existing calibration methods.
The paper tackled the problem of model overconfidence in glaucoma diagnosis by proposing V-ViT, a framework that integrates binocular information and metadata with a voting system to improve calibration, achieving state-of-the-art performance across all metrics.
Glaucoma is a major cause of irreversible blindness, with significant diagnostic subjectivity. This inherent uncertainty, combined with the overconfidence of models optimized solely for accuracy can lead to fatal issues such as overdiagnosis or missing critical diseases. To ensure clinical trust, model calibration is essential for reliable predictions, yet study in this field remains limited. Existing calibration study have overlooked glaucoma's systemic associations and high diagnostic subjectivity. To overcome these limitations, we propose V-ViT (Voting-based ViT), a framework that enhances calibration by integrating a patient's binocular information and metadata. Furthermore, to mitigate diagnostic subjectivity, V-ViT utilizes an iterative dropout-based Voting System to maximize calibration performance. The proposed framework achieved state-of-the-art performance across all metrics, including the primary calibration metrics. Our results demonstrate that V-ViT effectively resolves the issue of overconfidence in predictions in glaucoma diagnosis, providing highly reliable predictions for clinical use. Our source code is available at https://github.com/starforTJ/V-ViT.