A method comprising: staining a slice of cancerous tissue with a first stain; staining the slice with a second stain; wherein the first stain stains epithelial cells, wherein the first stain is taken from the group consisting of: pan cytokeratin, cytokeratin 18, α-methylacyl coenzyme A racemase (AMACR), cluster of differentiation 3 (CD3) antibody stain, cluster of differentiation 4 (CD4) antibody stain and cluster of differentiation 68 (CD68) antibody stain, and the second stain stains nuclei, wherein the second stain is taken from the group consisting of: fluorescent 4, 6-diamidino-2-phenylindole (DAPI) and hematoxylin, acquiring a first digital image of the slice; identifying a likely cancerous region in the first digital image based on the first stain; optimizing a plurality of parameters applied to associated mathematical operations to train a model distinguishing characteristics of the nuclei within a region that is stained by the first stain from nuclei that are outside that region based on the second stain but not on the first stain to classify individual pixels of the first digital image as belonging to the likely cancerous region, wherein the model is a convolutional neural network model, and wherein the optimizing the plurality of parameters involves training the convolutional neural network model; storing the plurality of parameters and associated mathematical operations of the model in a database; and applying the stored parameters and operations of the trained model to a second digital image to indicate a probability that each pixel of the second digital image falls within the likely cancerous region, wherein the second digital image is acquired from cancerous tissue that is stained with the second stain but not with the first stain. staining a slice of cancerous tissue with a first stain; staining the slice with a second stain; wherein the first stain stains epithelial cells, wherein the first stain is taken from the group consisting of: pan cytokeratin, cytokeratin 18, α-methylacyl coenzyme A racemase (AMACR), cluster of differentiation 3 (CD3) antibody stain, cluster of differentiation 4 (CD4) antibody stain and cluster of differentiation 68 (CD68) antibody stain, and the second stain stains nuclei, wherein the second stain is taken from the group consisting of: fluorescent 4, 6-diamidino-2-phenylindole (DAPI) and hematoxylin, acquiring a first digital image of the slice; identifying a likely cancerous region in the first digital image based on the first stain; optimizing a plurality of parameters applied to associated mathematical operations to train a model distinguishing characteristics of the nuclei within a region that is stained by the first stain from nuclei that are outside that region based on the second stain but not on the first stain to classify individual pixels of the first digital image as belonging to the likely cancerous region, wherein the model is a convolutional neural network model, and wherein the optimizing the plurality of parameters involves training the convolutional neural network model; storing the plurality of parameters and associated mathematical operations of the model in a database; and applying the stored parameters and operations of the trained model to a second digital image to indicate a probability that each pixel of the second digital image falls within the likely cancerous region, wherein the second digital image is acquired from cancerous tissue that is stained with the second stain but not with the first stain. <EOS>