  Unlike other histology analysis, classification of tubule status in testis
histology is very challenging due to their high similarity of texture and
shape. Traditional deep learning networks have difficulties to capture nuance
details among different tubule categories. In this paper, we propose a novel
deep learning architecture for feature learning, image classification, and
image reconstruction. It is based on stacked auto-encoders with an additional
layer, called a hyperlayer, which is created to capture features of an image at
different layers in the network. This addition effectively combines features at
different scales and thus provides a more complete profile for further
classification. Evaluation is performed on a set of 10,542 tubule image
patches. We demonstrate our approach with two experiments on two different
subsets of the dataset. The results show that the features learned from our
architecture achieve more than 98% accuracy and represent an improvement over
traditional deep network architectures.
