Systems and methods for generating virtually stained images of unstained samples are provided. According to an aspect of the invention, a method includes accessing an image training dataset including a plurality of image pairs. Each image pair includes a first image of an unstained first tissue sample, and a second image acquired when the first tissue sample is stained. The method also includes accessing a set of parameters for an artificial neural network, wherein the set of parameters includes weights associated with artificial neurons within the artificial neural network; training the artificial neural network by using the image training dataset and the set of parameters to adjust the weights; accessing a third image of a second tissue sample that is unstained; using the trained artificial neural network to generate a virtually stained image of the second tissue sample from the third image; and outputting the virtually stained image.