Triple Multi-scale Adversarial Learning with Self-attention and Quality Loss for Unpaired Fundus Fluorescein Angiography Synthesis

Clinically, the Fundus Fluorescein Angiography (FA) is a more common mean for Diabetic Retinopathy (DR) detection since the DR appears in FA much more contrasty than in Color Fundus Image (CF). However, acquiring FA has a risk of death due to the fluorescent allergy. Thus, in this paper, we explore a novel unpaired CycleGAN-based model for the FA synthesis from CF, where some strict structure similarity constraints are employed to guarantee the perfectly mapping from one domain to another one. First, a triple multi-scale network architecture with multi-scale inputs, multi-scale discriminators and multi-scale cycle consistency losses is proposed to enhance the similarity between two retinal modalities from different scales. Second, the self-attention mechanism is introduced to improve the adaptive domain mapping ability of the model. Third, to further improve strict constraints in the feather level, quality loss is employed between each process of generation and reconstruction. Qualitative examples, as well as quantitative evaluation, are provided to support the robustness and the accuracy of our proposed method.