  In ophthalmology, early fundus screening is an economic and effective way to
prevent blindness caused by ophthalmic diseases. Clinically, due to the lack of
medical resources, manual diagnosis is time-consuming and may delay the
condition. With the development of deep learning, some researches on ophthalmic
diseases have achieved good results, however, most of them are just based on
one disease. During fundus screening, ophthalmologists usually give diagnoses
of multi-disease on binocular fundus image, so we release a dataset with 8
diseases to meet the real medical scene, which contains 10,000 fundus images
from both eyes of 5,000 patients. We did some benchmark experiments on it
through some state-of-the-art deep neural networks. We found simply increasing
the scale of network cannot bring good results for multi-disease
classification, and a well-structured feature fusion method combines
characteristics of multi-disease is needed. Through this work, we hope to
advance the research of related fields.
