  In the real world, medical datasets often exhibit a long-tailed data
distribution (i.e., a few classes occupy most of the data, while most classes
have rarely few samples), which results in a challenging imbalance learning
scenario. For example, there are estimated more than 40 different kinds of
retinal diseases with variable morbidity, however with more than 30+ conditions
are very rare from the global patient cohorts, which results in a typical
long-tailed learning problem for deep learning-based screening models. In this
study, we propose class subset learning by dividing the long-tailed data into
multiple class subsets according to prior knowledge, such as regions and
phenotype information. It enforces the model to focus on learning the
subset-specific knowledge. More specifically, there are some relational classes
that reside in the fixed retinal regions, or some common pathological features
are observed in both the majority and minority conditions. With those subsets
learnt teacher models, then we are able to distill the multiple teacher models
into a unified model with weighted knowledge distillation loss. The proposed
framework proved to be effective for the long-tailed retinal diseases
recognition task. The experimental results on two different datasets
demonstrate that our method is flexible and can be easily plugged into many
other state-of-the-art techniques with significant improvements.
