Heterogeneity characterization of immunohistochemistry stained tissue using convolutional autoencoder
The focus of this paper is to illustrate how computational image processing and machine learning can help address two of the challenges of histological image analysis, namely, the cellular heterogeneity, and the imprecise labeling. We propose an unsupervised method of generating representative image signatures based on an autoencoder architecture which reduces the dependency on labels that tend to be imprecise and tedious to get. We have modified and enhanced the architecture to simultaneously produce representative image features as well as perform dictionary learning on these features to enable robust characterization of the cellular phenotypes. We integrate the extracted features in a disease grading framework, test it in prostate tissues immunostained for different protein visualization and show significant improvement in terms of grading accuracy compared to alternative supervised feature-extraction methods.