Stain Normalization Using Sparse AutoEncoders (StaNoSA): Application to Digital Pathology

Overview

Journal Comput Med Imaging Graph

Specialty Radiology

Date 2016 Jul 5

PMID 27373749

Citations 68

Authors

Andrew Janowczyk

Ajay Basavanhally

Anant Madabhushi

Affiliations

Soon will be listed here.

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.

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