  Fundus photography is an essential examination for clinical and differential
diagnosis of fundus diseases. Recently, Ultra-Wide-angle Fundus (UWF)
techniques, UWF Fluorescein Angiography (UWF-FA) and UWF Scanning Laser
Ophthalmoscopy (UWF-SLO) have been gradually put into use. However, Fluorescein
Angiography (FA) and UWF-FA require injecting sodium fluorescein which may have
detrimental influences. To avoid negative impacts, cross-modality medical image
generation algorithms have been proposed. Nevertheless, current methods in
fundus imaging could not produce high-resolution images and are unable to
capture tiny vascular lesion areas. This paper proposes a novel conditional
generative adversarial network (UWAT-GAN) to synthesize UWF-FA from UWF-SLO.
Using multi-scale generators and a fusion module patch to better extract global
and 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 new
weighted losses on different scales of data. Experiments on an in-house UWF
image dataset demonstrate the superiority of the UWAT-GAN over the
state-of-the-art methods. The source code is available at:
https://github.com/Tinysqua/UWAT-GAN.
