For example, when converting between image modalities, it may be impossible to obtain pixel-scale paired images using different image modalities. However, in some cases, obtaining paired datasets may be difficult or even impossible. Most model architectures for image translation tasks require paired-image datasets. The most common models used for image translation tasks are generative adversarial networks or their variants (Section Generative adversarial networks and their variants). In computational pathology, image-to-image translation has been explored for stain color normalization (Section Stain color normalization) and for converting between different image modalities (Section Mode switching). This can be applied to a wide variety of applications, such as converting between night and day images, winter and summer images, or more useful tasks like converting satellite images to maps. The goal is to learn the transformation between the input and output images. Image translation refers to the conversion of one image representation to another image representation. Richard Levenson MD, in Artificial Intelligence and Deep Learning in Pathology, 2021 Image translation and style transfer Applications of artificial intelligence for image enhancement in pathology
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