Abstract: 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.

Abstract: A computer implemented method for recognizing facial expressions by applying feature learning and feature engineering to face images. The method includes conducting feature learning on a face image comprising feeding the face image into a first convolution neural network to obtain a first decision, conducting feature engineering on a face image, comprising the steps of automatically detecting facial landmarks in the face image, transforming the facial features into a two-dimensional matrix, and feeding the two-dimensional matrix into a second convolution neural network to obtain a second decision, computing a hybrid decision based on the first decision and the second decision, and recognizing a facial expression in the face image in accordance to the hybrid decision.

Abstract: 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.