Using Expert Gaze for Self-Supervised and Supervised Contrastive Learning of Glaucoma from OCT Data

Wai Tak Lau, Ye Tian, Roshan Kenia, Saanvi Aima, Kaveri A. Thakoor

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Abstract: In this work, we address the challenge of limited data availability common in healthcare settings by using clinician (ophthalmologist) gaze data on optical coherence tomography (OCT) report images as they diagnose glaucoma, a top cause of irreversible blindness world-wide. We directly learn gaze representations with our 'GazeFormerMD' model to generate pseudo-labels using a novel multi-task objective, combining triplet and cross-entropy losses. We use these pseudo-labels for weakly supervised contrastive learning (WSupCon) to detect glaucoma from a partially-labeled dataset of OCT report images. Our natural-language-inspired region-based-encoding GazeFormerMD model pseudo-labels, trained using our multi-task objective, enable downstream glaucoma detection accuracy via WSupCon exceeding 91% even with only 70% labeled training data. Furthermore, a model pre-trained with GazeFormerMD-generated pseudo-labels and used for linear evaluation on an unseen OCT-report dataset achieved comparable performance to a fully-supervised, trained-from-scratch model while using only 25% labeled data.