A method of training a tissue classifier for performing quantitative histopathological assessment, comprising: converting a slide image into a binary slide image, wherein the slide image is selected from a library of control images, wherein each slide image in the library of control images is associated with a pass classification or a fail classification; detecting one or more nuclei within the binary slide image; for each detected nucleus, extracting a feature from the detected nucleus, wherein the feature represents a property of the detected nucleus within the binary slide image and comprises at least one of an area of the detected nucleus, a perimeter of the detected nucleus, an integrated density of the detected nucleus, or a circularity of the detected nucleus; for each detected nucleus, generating, based on the feature, a feature fingerprint associated with the binary slide image, wherein the feature fingerprint is a numerical value calculated from processing the feature; incorporating the binary slide image into a cluster wherein the cluster comprises a plurality of images, wherein the plurality of images comprises a first plurality of images, wherein each of the first plurality of images is associated with a corresponding feature fingerprint, and wherein the incorporating is based on comparing the feature fingerprint of the binary slide image with the corresponding feature fingerprint of each of the first plurality of images; applying a cutoff height to the cluster to form a plurality of groups, wherein the cutoff height minimizes a number of groups within the plurality of groups based on multivariate analysis of variance analysis of the cluster; categorizing a first group within the plurality of groups as a positive control set if the first group comprises first slide images associated with the pass classification; and categorizing a second group within the plurality of groups as a negative control set if the second group comprises second slide images associated with the fail classification. converting a slide image into a binary slide image, wherein the slide image is selected from a library of control images, wherein each slide image in the library of control images is associated with a pass classification or a fail classification; detecting one or more nuclei within the binary slide image; for each detected nucleus, extracting a feature from the detected nucleus, wherein the feature represents a property of the detected nucleus within the binary slide image and comprises at least one of an area of the detected nucleus, a perimeter of the detected nucleus, an integrated density of the detected nucleus, or a circularity of the detected nucleus; for each detected nucleus, generating, based on the feature, a feature fingerprint associated with the binary slide image, wherein the feature fingerprint is a numerical value calculated from processing the feature; incorporating the binary slide image into a cluster wherein the cluster comprises a plurality of images, wherein the plurality of images comprises a first plurality of images, wherein each of the first plurality of images is associated with a corresponding feature fingerprint, and wherein the incorporating is based on comparing the feature fingerprint of the binary slide image with the corresponding feature fingerprint of each of the first plurality of images; applying a cutoff height to the cluster to form a plurality of groups, wherein the cutoff height minimizes a number of groups within the plurality of groups based on multivariate analysis of variance analysis of the cluster; categorizing a first group within the plurality of groups as a positive control set if the first group comprises first slide images associated with the pass classification; and categorizing a second group within the plurality of groups as a negative control set if the second group comprises second slide images associated with the fail classification. <EOS>