Fig. 3

Computational models for Digital staining. a The general supervised machine learning workflow for most digital staining. Please refer to the main text for some examples that use an unsupervised workflow b The most commonly used models: besides earlier implementations of color-coding with a linear contrast translation equation f(k) or feature engineering and classical ML, almost all modern digital staining implementations use deep learning with either CNN and GAN architectures (I = Input image, T = Target image, G = Generator, \(I_g\) = generated image, D = Discriminator)