  Hard exudates (HE) is the most specific biomarker for retina edema. Precise
HE segmentation is vital for disease diagnosis and treatment, but automatic
segmentation is challenged by its large variation of characteristics including
size, shape and position, which makes it difficult to detect tiny lesions and
lesion boundaries. Considering the complementary features between segmentation
and super-resolution tasks, this paper proposes a novel hard exudates
segmentation method named SS-MAF with an auxiliary super-resolution task, which
brings in helpful detailed features for tiny lesion and boundaries detection.
Specifically, we propose a fusion module named Multi-scale Attention Fusion
(MAF) module for our dual-stream framework to effectively integrate features of
the two tasks. MAF first adopts split spatial convolutional (SSC) layer for
multi-scale features extraction and then utilize attention mechanism for
features fusion of the two tasks. Considering pixel dependency, we introduce
region mutual information (RMI) loss to optimize MAF module for tiny lesions
and boundary detection. We evaluate our method on two public lesion datasets,
IDRiD and E-Ophtha. Our method shows competitive performance with
low-resolution inputs, both quantitatively and qualitatively. On E-Ophtha
dataset, the method can achieve $\geq3\%$ higher dice and recall compared with
the state-of-the-art methods.
