Multi-Label Class-Incremental Learning for Ophthalmic Diagnosis over a Recurring Population

Many real-world problems require that each example be assigned to multiple non-exclusive labels, a task known as multi-label classification. However, most of the problems considered in the context of continual learning, including the class-incremental learning (CIL) scenario, assume a single-label classification task, which does not allow an object to be assigned multiple labels simultaneously. This paper addresses that gap by introducing a multi-label incremental learning scenario that removes the assumptions of disjoint tasks and mutually exclusive classes. This scenario is particularly relevant in oculomics, which studies the relationship between ophthalmic biomarkers and systemic health or diseases. In this field, new sets of diagnoses often coexist with existing classes rather than replacing them, and re-annotation for each new problem is rarely feasible, leaving most historical images only partially labeled. We identify and formalize this scenario as a multi-label class-incremental learning problem over a recurring population, with a dynamic label space and partial relabeling, in which each label may be positive, negative, or unobserved. The label space thus expands from task to task, and training examples recur across different tasks as new sets of labels are introduced; to our knowledge, this combination has not been studied before. We evaluate the setting experimentally on the ODIR-5K dataset, a real-world multi-label ophthalmology benchmark, by instantiating the case in which every introduced label is observed, so that missingness is driven solely by the schedule. Buffer-free continual learning baselines are compared against joint and naive bounds. About half of the gap between sequential and joint training is due to forgetting, and half to lost co-training, which no buffer-free method recovers. Refitting the decision layer on an unchanged backbone recovers three-quarters of the gap — placing the residual loss in the heads rather than in the shared representation. Our findings guide the implementation and maintenance of diagnostic models whose label sets evolve.