Abstract: Methods and systems for pre-processing immunocytochemistry images for machine learning are provided. The pre-processing method includes receiving paired positive and negative multi-protein images and segmenting stains of the paired multi-protein images. The method also includes labelling each image pixel corresponding to a cell of the paired multi-protein images and translating image pixel information of the labelled image pixels to tabular form to generate a table of cell coordinates and geometrical characteristics for each of the paired multi-protein images. The method further includes generating two cell-paired tables for each of the paired multi-protein images based on Euclidean distance-based pairing prior to input for the machine learning, where the Euclidean distance-based pairing is based on the cell coordinates in the table of cell coordinates and geometrical characteristics for each of the paired multi-protein images.

Abstract: A system and method for generating a stained image including the steps of obtaining a first image of a key sample section; and processing the first image with a multi-modal stain learning engine arranged to generate at least one stained image, wherein the at least one stained image represents the key sample section stained with at least one stain.

Abstract: A system and method for generating a stained image including the steps of obtaining a first image of a key sample section; and processing the first image with a multi-modal stain learning engine arranged to generate at least one stained image, wherein the at least one stained image represents the key sample section stained with at least one stain.

Abstract: A system and method for generating a stained image including the steps of obtaining a first image of a key sample section; and processing the first image with a stain learning engine arranged to generate at least one stained image, wherein the at least one stained image represents the key sample section stained with at least one stain.

Abstract: A system and method for generating a stained image including the steps of obtaining a first image of a key sample section; and processing the first image with a stain learning engine arranged to generate at least one stained image, wherein the at least one stained image represents the key sample section stained with at least one stain.

Abstract: Certain biomarkers and biomarker combinations are useful in a qualifying lung cancer status in a subject. A diagnostic methodology employing these biomarkers and combinations can detect whether a subject has lung cancer.