University of Denver

Ali Mollahosseini

Mohammad H Mahoor

Hadi Rezaeilouyeh

Quinn Martindale-George

Luke Skelly

Various arrangements for identifying and grading cancer in tissue samples are presented. A digital image of a stained tissue sample may be acquired. A Shearlet transform may be performed on the digital image of the stained tissue sample. Shearlet coefficients may be calculated based on the performed Shearlet transform of the normalized digital RGB image of the stained tissue sample. A trained neural network may be applied to create a plurality of feature maps using the digital image and Shearlet coefficients, wherein the trained neural network was trained using a plurality of images and Shearlet coefficients of a plurality of digital images. A classifier may be applied to an output of the trained neural network to identify whether cancer is present in the stained tissue sample. A notification may be output that is indicative of a grade of detected cancer in the sample.

Methods and systems for human tissue analysis using shearlet transforms

Some embodiments of the invention are directed toward a robotic head that includes a head enclosure; a translucent face mask coupled with the head enclosure, having a front side and a rear side; a neck system coupled with the head enclosure and configured to move the head enclosure; and a projector coupled with the head enclosure and disposed to project images of a face onto the rear side of the face mask.

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Denver, CO, United States of America

Ali Mollahosseini