A method for analyzing a digital pathology image comprising: selecting at least one analysis region in the digital pathology image; separating the at least one analysis region into a tissue region and a background region using a predetermined separation threshold; identifying a plurality of nuclear prospects within the tissue region, wherein identifying the plurality of nuclear prospects includes: calculating a Principal Component Analysis, PCA projection of the tissue region of the digital pathology image, segmenting the PCA projection of the tissue region of the digital pathology image into a plurality of image layers via a spectral hashing function, and determining the plurality of nuclear prospects from among the plurality of layers; selecting nuclei from among the identified nuclear prospects, wherein selecting nuclei includes: generating a hierarchical connected graph associating at least a first one of the plurality of nuclear prospects on a first segmented layer of the digital image with at least a second one of the plurality of nuclear prospects on a second segmented layer of the digital image, comparing the first one of the nuclear prospects to the second one of the nuclear prospects to identify the nuclear prospect having a higher convexity score as superior nuclear prospect, designating the superior nuclear prospect as a selected nuclei; and generating a diagnostic score of the at least one analysis region based on the selected nuclei. selecting at least one analysis region in the digital pathology image; separating the at least one analysis region into a tissue region and a background region using a predetermined separation threshold; identifying a plurality of nuclear prospects within the tissue region, wherein identifying the plurality of nuclear prospects includes: calculating a Principal Component Analysis, PCA projection of the tissue region of the digital pathology image, segmenting the PCA projection of the tissue region of the digital pathology image into a plurality of image layers via a spectral hashing function, and determining the plurality of nuclear prospects from among the plurality of layers; calculating a Principal Component Analysis, PCA projection of the tissue region of the digital pathology image, segmenting the PCA projection of the tissue region of the digital pathology image into a plurality of image layers via a spectral hashing function, and determining the plurality of nuclear prospects from among the plurality of layers; selecting nuclei from among the identified nuclear prospects, wherein selecting nuclei includes: generating a hierarchical connected graph associating at least a first one of the plurality of nuclear prospects on a first segmented layer of the digital image with at least a second one of the plurality of nuclear prospects on a second segmented layer of the digital image, comparing the first one of the nuclear prospects to the second one of the nuclear prospects to identify the nuclear prospect having a higher convexity score as superior nuclear prospect, designating the superior nuclear prospect as a selected nuclei; and generating a hierarchical connected graph associating at least a first one of the plurality of nuclear prospects on a first segmented layer of the digital image with at least a second one of the plurality of nuclear prospects on a second segmented layer of the digital image, comparing the first one of the nuclear prospects to the second one of the nuclear prospects to identify the nuclear prospect having a higher convexity score as superior nuclear prospect, designating the superior nuclear prospect as a selected nuclei; and generating a diagnostic score of the at least one analysis region based on the selected nuclei. <EOS>