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

TitleStain Normalization using Sparse AutoEncoders (StaNoSA): Application to digital pathology.
Publication TypeJournal Article
Year of Publication2017
AuthorsJanowczyk, A, Basavanhally A, Madabhushi A
JournalComputerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Date Published2017 Apr

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.

PDF Link

Alternate JournalComput Med Imaging Graph

 *IEEE COPYRIGHT NOTICE: 1997 IEEE. * Personal use of this material is permitted. However, permission to reprint/ republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

*COPYRIGHT NOTICE:* These materials are presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.