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Fundus photography is an essential examination for clinical and differentialdiagnosis of fundus diseases. Recently, Ultra-Wide-angle Fundus (UWF)techniques, UWF Fluorescein Angiography (UWF-FA) and UWF Scanning LaserOphthalmoscopy (UWF-SLO) have been gradually put into use. However, FluoresceinAngiography (FA) and UWF-FA require injecting sodium fluorescein which may havedetrimental influences. To avoid negative impacts, cross-modality medical imagegeneration algorithms have been proposed. Nevertheless, current methods infundus imaging could not produce high-resolution images and are unable tocapture tiny vascular lesion areas. This paper proposes a novel conditionalgenerative adversarial network (UWAT-GAN) to synthesize UWF-FA from UWF-SLO.Using multi-scale generators and a fusion module patch to better extract globaland local information, our model can generate high-resolution images. Moreover,an attention transmit module is proposed to help the decoder learn effectively.Besides, a supervised approach is used to train the network using multiple newweighted losses on different scales of data. Experiments on an in-house UWFimage dataset demonstrate the superiority of the UWAT-GAN over thestate-of-the-art methods. The source code is available at:https://github.com/Tinysqua/UWAT-GAN.

UWAT-GAN: Fundus Fluorescein Angiography Synthesis via Ultra-wide-angle Transformation Multi-scale GAN