arXiv:2503.18642v2 Announce Type: replace-cross 
Abstract: 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.

تسلط دراسة حديثة الضوء على أهمية معايرة النماذج في تشخيص الزرق، وهو أحد الأسباب الرئيسية للعمى غير القابل للعكس. من خلال دمج طرق ثنائية العين المعتمدة على التصويت والبيانات الوصفية، يسعى الباحثون إلى تقليل ذاتية التشخيص وتحسين موثوقية التنبؤات. هذه الخطوة مهمة لأنها تعالج مخاطر التشخيص الزائد أو تفويت الأمراض الحرجة، مما يعزز الثقة السريرية في نماذج التشخيص.

Un estudio reciente destaca la importancia de la calibración de modelos en el diagnóstico del glaucoma, una de las principales causas de ceguera irreversible. Al integrar métodos binoculares basados en votación y metadatos, los investigadores buscan reducir la subjetividad del diagnóstico y mejorar la fiabilidad de las predicciones. Este avance es crucial, ya que aborda los riesgos de sobrediagnóstico y de pasar por alto enfermedades críticas, mejorando así la confianza clínica en los modelos de diagnóstico.

Une étude récente souligne l'importance de la calibration des modèles dans le diagnostic du glaucome, une cause majeure de cécité irréversible. En intégrant des méthodes binoculaires basées sur le vote et des métadonnées, les chercheurs visent à réduire la subjectivité du diagnostic et à améliorer la fiabilité des prédictions. Cette avancée est cruciale car elle répond aux risques de surdiagnostic et de maladies critiques manquées, renforçant ainsi la confiance clinique dans les modèles de diagnostic.

A recent study highlights the importance of model calibration in glaucoma diagnosis, a leading cause of irreversible blindness. By integrating voting-based binocular methods and metadata, researchers aim to reduce diagnostic subjectivity and improve prediction reliability. This advancement is crucial as it addresses the risks of overdiagnosis and missed critical diseases, ultimately enhancing clinical trust in diagnostic models.

Rethinking Glaucoma Calibration: Voting-Based Binocular and Metadata Integration

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