  Diabetic retinopathy is the most important complication of diabetes. Early
diagnosis of retinal lesions helps to avoid visual loss or blindness. Due to
high-resolution and small-size lesion regions, applying existing methods, such
as U-Nets, to perform segmentation on fundus photography is very challenging.
Although downsampling the input images could simplify the problem, it loses
detailed information. Conducting patch-level analysis helps reaching fine-scale
segmentation yet usually leads to misunderstanding as the lack of context
information. In this paper, we propose an efficient network that combines them
together, not only being aware of local details but also taking fully use of
the context perceptions. This is implemented by integrating the decoder parts
of a global-level U-net and a patch-level one. The two streams are jointly
optimized, ensuring that they are enhanced mutually. Experimental results
demonstrate our new framework significantly outperforms existing patch-based
and global-based methods, especially when the lesion regions are scattered and
small-scaled.
