  Automatic diabetic retinopathy (DR) grading based on fundus photography has
been widely explored to benefit the routine screening and early treatment.
Existing researches generally focus on single-field fundus images, which have
limited field of view for precise eye examinations. In clinical applications,
ophthalmologists adopt two-field fundus photography as the dominating tool,
where the information from each field (i.e.,macula-centric and optic
disc-centric) is highly correlated and complementary, and benefits
comprehensive decisions. However, automatic DR grading based on two-field
fundus photography remains a challenging task due to the lack of publicly
available datasets and effective fusion strategies. In this work, we first
construct a new benchmark dataset (DRTiD) for DR grading, consisting of 3,100
two-field fundus images. To the best of our knowledge, it is the largest public
DR dataset with diverse and high-quality two-field images. Then, we propose a
novel DR grading approach, namely Cross-Field Transformer (CrossFiT), to
capture the correspondence between two fields as well as the long-range spatial
correlations within each field. Considering the inherent two-field geometric
constraints, we particularly define aligned position embeddings to preserve
relative consistent position in fundus. Besides, we perform masked cross-field
attention during interaction to flter the noisy relations between fields.
Extensive experiments on our DRTiD dataset and a public DeepDRiD dataset
demonstrate the effectiveness of our CrossFiT network. The new dataset and the
source code of CrossFiT will be publicly available at
https://github.com/FDU-VTS/DRTiD.
