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, where producing the final classification comprises: comparing the CNN classification score to the HC classification score, and upon determining that the CNN classification score and the HC classification score are not within a threshold range: training a cascaded classifier using a stacked set of features, where the stacked set of features comprises the set of CNN-learned features and the set of HC features; generating a cascaded classification score by classifying the candidate mitosis patch with the cascaded classifier, and producing a final classification, based, at least in part, on a weighted average of the CNN classification score, the HC classification score, and the cascaded classification score, where the final classification indicates the probability that the mitosis patch is mitotic.
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, where producing the final classification comprises: comparing the CNN classification score to the HC classification score, and upon determining that the CNN classification score and the HC classification score are not within a threshold range: training a cascaded classifier using a stacked set of features, where the stacked set of features comprises the set of CNN-learned features and the set of HC features; generating a cascaded classification score by classifying the candidate mitosis patch with the cascaded classifier, and producing a final classification, based, at least in part, on a weighted average of the CNN classification score, the HC classification score, and the cascaded classification score, where the final classification indicates the probability that the mitosis patch is mitotic. 10. The non-transitory computer-readable storage medium of claim 1 , where extracting the set of HC features from the candidate mitosis patch comprises: extracting a set of morphology features from the candidate mitosis patch; extracting a set of intensity features from the candidate mitosis patch, and extracting a set of texture features from the candidate mitosis patch.