Calibrated Hybrid CNN-Transformer for Retinal OCT Classification

• URL: http://arxiv.org/abs/2607.09809v1
• Date: Thu, 09 Jul 2026 23:23:56 GMT
• Title: Calibrated Hybrid CNN-Transformer for Retinal OCT Classification
• Abstract summary: Deep models for retinal optical coherence tomography ( OCT) classification report high accuracy but rarely report whether their confidence can be trusted.<n>We pair a hybrid convolutional-Transformer encoder with a gradient-boosting (XGBoost) classification head and a three-part clinical safety layer: confidence calibration, out-of-distribution (OOD) rejection, and per-prediction uncertainty flagging.<n>On four-class OCT (84,495 scans) the model reaches 95.4% accuracy while cutting calibration error twelve-fold (expected calibration error, ECE = 0.0024), so the confidence it reports tracks its true accuracy.
• Score: 1.0817607291351121
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep models for retinal optical coherence tomography (OCT) classification report high accuracy but rarely report whether their confidence can be trusted -- a gap that matters when a wrong-but-confident reading delays sight-saving treatment. We pair a hybrid convolutional-Transformer encoder with a gradient-boosting (XGBoost) classification head and a three-part clinical safety layer: confidence calibration, out-of-distribution (OOD) rejection, and per-prediction uncertainty flagging. On four-class OCT (84,495 scans) the model reaches 95.4% accuracy while cutting calibration error twelve-fold (expected calibration error, ECE = 0.0024), so the confidence it reports tracks its true accuracy. To our knowledge this is the first OCT classifier to validate all three safety mechanisms jointly, with public weights and reproducible multi-seed evaluation.

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