1. A non-transitory computer-readable storage medium storing computer-executable instructions that when executed by a computer control the computer to perform a method for detecting cellular mitosis in a region of cancerous tissue, the method comprising: acquiring an image of cancerous tissue; segmenting the image into a candidate mitosis patch; extracting a set of convolutional neural network (CNN) learned features from the candidate mitosis patch using a CNN; training a CNN classifier using the set of CNN-learned features; generating a CNN classification score by classifying the candidate mitosis patch with the CNN classifier; extracting a set of hand-crafted (HC) features from the candidate mitosis patch; training an HC classifier using the set of HC features; generating an HC classification score by classifying the candidate mitosis patch with the HC classifier; producing a final classification based, at least in part, on both the CNN classification score and the HC classification score; controlling an automated mitotic nuclei detection system to classify the candidate mitosis patch as mitotic or non-mitotic based on the final classification; generating a mitotic count by summing the number of candidate mitosis patches classified as mitotic by the automated mitotic nuclei detection system; and controlling an automated cancer grading system to grade the image using a Bloom-Richardson grade, where the Bloom-Richardson grade is based, at least in part, on the mitotic count.