aiqbench analysis
This research introduces ToRe, a parameter-efficient framework that addresses catastrophic forgetting in ophthalmic deep learning caused by severe class imbalance and style shifts. By leveraging frozen foundation models and token-adaptive recursion, the method dynamically allocates computational depth to complex tokens associated with rare lesions without requiring rehearsal data. The approach demonstrates robust generalization across heterogeneous clinical datasets while maintaining near-zero forgetting, offering a practical solution for adapting diagnostic models in dynamic medical environments.
Learning When to Recur: Token-Adaptive Recursion for Imbalanced Ophthalmic Domain Incremental Learning
arXiv:2609.32785v1 Announce Type: new Abstract: Domain incremental learning is essential for adapting ophthalmic deep learning models to sequential clinical domains while preserving diagnostic expertise. Existing domain incremental learning methods predominantly address the domain shift induced by style variations. However, they often overlook the severe class imbalance inherent in real-world clinical scenarios, such as clinical referral systems. Institutions in these systems encounter drastic fluctuations in class priors, resulting in label distribution shift, a critical form of domain shift that triggers severe catastrophic forgetting. To address these challenges, we propose ToRe, a rehearsal-free and parameter-efficient framework that leverages frozen ophthalmic foundation models for robust incremental adaptation. ToRe employs a parameter isolation strategy to decouple domain-specific optimization paths, thereby helping mitigate catastrophic forgetting driven by both label distribution shift and style variations. Simultaneously, it introduces token-adaptive recursion that adaptively allocates additional computational depth across tokens, allowing simple tokens to exit the recursion loop early while subjecting complex tokens, such as those associated with lesions, to deeper recursive processing. This mechanism enhances the feature representations for minority classes, thereby supporting generalization throughout the domain incremental learning process. Extensive evaluations on nine heterogeneous datasets demonstrate that ToRe consistently outperforms state-of-the-art methods in overall performance across the three benchmarks, while maintaining near-zero forgetting. Together, these results support the applicability of ToRe to dynamic and imbalanced clinical environments. The code is available at https://github.com/Nancyolo/ToRe
Abstract:Domain incremental learning is essential for adapting ophthalmic deep learning models to sequential clinical domains while preserving diagnostic expertise. Existing domain incremental learning methods predominantly address the domain shift induced by style variations. However, they often overlook the severe class imbalance inherent in real-world clinical scenarios, such as clinical referral systems. Institutions in these systems encounter drastic fluctuations in class priors, resulting in label distribution shift, a critical form of domain shift that triggers severe catastrophic forgetting. To address these challenges, we propose ToRe, a rehearsal-free and parameter-efficient framework that leverages frozen ophthalmic foundation models for robust incremental adaptation. ToRe employs a parameter isolation strategy to decouple domain-specific optimization paths, thereby helping mitigate catastrophic forgetting driven by both label distribution shift and style variations. Simultaneously, it introduces token-adaptive recursion that adaptively allocates additional computational depth across tokens, allowing simple tokens to exit the recursion loop early while subjecting complex tokens, such as those associated with lesions, to deeper recursive processing. This mechanism enhances the feature representations for minority classes, thereby supporting generalization throughout the domain incremental learning process. Extensive evaluations on nine heterogeneous datasets demonstrate that ToRe consistently outperforms state-of-the-art methods in overall performance across the three benchmarks, while maintaining near-zero forgetting. Together, these results support the applicability of ToRe to dynamic and imbalanced clinical environments. The code is available at this https URL
Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
arXiv:2609.32785 [cs.LG]
(or arXiv:2609.32785v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.32785 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 324 words
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