Heterogeneity characterization of immunohistochemistry stained tissue using convolutional autoencoder

Erwan Zerhouni; Bogdan Prisacari; Qing Zhong; Peter Wild; Maria Gabrani

doi:10.1117/12.2256238

1 March 2017 Heterogeneity characterization of immunohistochemistry stained tissue using convolutional autoencoder

Erwan Zerhouni, Bogdan Prisacari, Qing Zhong, Peter Wild, Maria Gabrani

Author Affiliations +

Proceedings Volume 10140, Medical Imaging 2017: Digital Pathology; 101400P (2017) https://doi.org/10.1117/12.2256238
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

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.

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Erwan Zerhouni, Bogdan Prisacari, Qing Zhong, Peter Wild, and Maria Gabrani "Heterogeneity characterization of immunohistochemistry stained tissue using convolutional autoencoder", Proc. SPIE 10140, Medical Imaging 2017: Digital Pathology, 101400P (1 March 2017); https://doi.org/10.1117/12.2256238

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