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Comput Med Imaging Graph. 2017 Apr;57:50-61. doi: 10.1016/j.compmedimag.2016.05.003. Epub 2016 May 16.

Stain Normalization using Sparse AutoEncoders (StaNoSA): Application to digital pathology.

Author information

1
Case Western Reserve University, Cleveland, OH, United States. Electronic address: andrew.janowczyk@case.edu.
2
Inspirata, Inc., Tampa, FL, United States.
3
Case Western Reserve University, Cleveland, OH, United States.

Abstract

Digital histopathology slides have many sources of variance, and while pathologists typically do not struggle with them, computer aided diagnostic algorithms can perform erratically. This manuscript presents Stain Normalization using Sparse AutoEncoders (StaNoSA) for use in standardizing the color distributions of a test image to that of a single template image. We show how sparse autoencoders can be leveraged to partition images into tissue sub-types, so that color standardization for each can be performed independently. StaNoSA was validated on three experiments and compared against five other color standardization approaches and shown to have either comparable or superior results.

KEYWORDS:

Deep learning; Digital histopathology; Image processing; Stain Normalization

PMID:
27373749
PMCID:
PMC5112159
DOI:
10.1016/j.compmedimag.2016.05.003
[Indexed for MEDLINE]
Free PMC Article

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