  In the real world, medical datasets often exhibit a long-tailed data
distribution (i.e., a few classes occupy the majority of the data, while most
classes have only a limited number of samples), which results in a challenging
long-tailed learning scenario. Some recently published datasets in
ophthalmology AI consist of more than 40 kinds of retinal diseases with complex
abnormalities and variable morbidity. Nevertheless, more than 30 conditions are
rarely seen in global patient cohorts. From a modeling perspective, most deep
learning models trained on these datasets may lack the ability to generalize to
rare diseases where only a few available samples are presented for training. In
addition, there may be more than one disease for the presence of the retina,
resulting in a challenging label co-occurrence scenario, also known as
\textit{multi-label}, which can cause problems when some re-sampling strategies
are applied during training. To address the above two major challenges, this
paper presents a novel method that enables the deep neural network to learn
from a long-tailed fundus database for various retinal disease recognition.
Firstly, we exploit the prior knowledge in ophthalmology to improve the feature
representation using a hierarchy-aware pre-training. Secondly, we adopt an
instance-wise class-balanced sampling strategy to address the label
co-occurrence issue under the long-tailed medical dataset scenario. Thirdly, we
introduce a novel hybrid knowledge distillation to train a less biased
representation and classifier. We conducted extensive experiments on four
databases, including two public datasets and two in-house databases with more
than one million fundus images. The experimental results demonstrate the
superiority of our proposed methods with recognition accuracy outperforming the
state-of-the-art competitors, especially for these rare diseases.
