Learning When to Recur: Token-Adaptive Recursion for Imbalanced Ophthalmic Domain Incremental Learning

arXiv:2609.32785 · cs.LG, cs.CV · Submitted 2026-09-26 · Read on arXiv

cs.LG, cs.CV

Submitted: 2026-09-26

Updated: 2026-09-26

Code: https://github.com/Nancyolo/ToRe

Terminology

• Benchmark — 37× in this paper · explained in 2 episodes
• Accuracy — 17× in this paper · explained in 1 episode
• Continual Learning — 14× in this paper · explained in 2 episodes
• Adam — 9× in this paper · explained in 2 episodes
• Adaptation — 9× in this paper · explained in 1 episode
• Catastrophic Forgetting — 9× in this paper · explained in 5 episodes
• Distribution Shift — 9× in this paper · explained in 5 episodes
• Domain Shift — 8× in this paper · explained in 4 episodes
• F1 Score — 8× in this paper · explained in 3 episodes
• Foundation Model — 8× in this paper · explained in 3 episodes
• Transformer — 7× in this paper · explained in 2 episodes
• Fine-Tuning — 6× in this paper · explained in 5 episodes
• AUROC — 4× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Foundation Models — 3× in this paper · explained in 7 episodes
• Regularization — 3× in this paper · explained in 3 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Asia Pacific Tele-Ophthalmology Society — 2× in this paper
• Conference on Computer Vision and Pattern Recognition — 2× in this paper
• Core — 2× in this paper · explained in 2 episodes
• Cosine Similarity — 2× in this paper · explained in 3 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Parameter-Efficient Fine-Tuning — 2× in this paper · explained in 2 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Straight-Through Estimator — 2× in this paper
• Ablation study — 1× in this paper · explained in 1 episode
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Data Heterogeneity — 1× in this paper · explained in 3 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Foundation Models (FMs) — 1× in this paper · explained in 2 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Human in the Loop Learning — 1× in this paper
• Interpretability — 1× in this paper · explained in 15 episodes
• Parameter-Efficient Fine-Tuning (PEFT) — 1× in this paper · explained in 4 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes
• Two-stage training — 1× in this paper · explained in 2 episodes
• nnU-Net — 1× in this paper · explained in 2 episodes

Sources

• HYAMD High-Resolution Fundus Image Dataset for age related macular degeneration (AMD) Diagnosis
• EyeFound: A Multimodal Generalist Foundation Model for Ophthalmic Imaging

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