通过双眼信息与元数据融合，提升青光眼诊断模型的可靠性。
Rethinking Glaucoma Calibration: Voting-Based Binocular and Metadata Integration
- 用投票机制融合双眼数据和患者元数据，改进模型校准。
- 在多个校准指标上达到当前最优，显著降低预测过自信问题。
- 适合临床依赖高可信度诊断的场景，尤其关注误诊风险。
青光眼是导致不可逆失明的主要原因，其诊断具有显著主观性。这种内在不确定性，加上仅以准确率优化的模型所产生的过度自信，可能导致严重后果，如过度诊断或漏诊。为确保临床信任，模型校准对可靠预测至关重要，但相关研究仍较匮乏。现有校准研究忽视了青光眼的系统性关联及高诊断主观性。为此，我们提出V-ViT（基于投票的ViT）框架，通过整合患者的双眼信息与元数据来增强校准能力。此外，为缓解诊断主观性，V-ViT采用基于迭代丢弃的投票机制，最大化校准性能。该框架在所有指标上均达到当前最优表现，包括主要校准指标。结果表明，V-ViT有效缓解了青光眼诊断中预测过度自信的问题，为临床应用提供高度可靠的预测。代码已公开于 https://github.com/starforTJ/V-ViT。
原文摘要 · Abstract (English)
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.
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