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.
