Patent Document ID: 9430829
Application ID: 14562883
Patent Status: 1

Claim One:
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.