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

•Digital histopathology slides have many sources of variance.•These variances can cause algorithms to perform erratically.•Stain Normalization using Sparse AutoEncoders (StaNoSA) in introduced.•It standardizes color distributions of a test image to a single template image.•Validated using three expe...

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Vydáno v:	Computerized medical imaging and graphics Ročník 57; s. 50 - 61
Hlavní autoři:	Janowczyk, Andrew, Basavanhally, Ajay, Madabhushi, Anant
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Elsevier Ltd 01.04.2017 Elsevier Science Ltd
Témata:	Algorithms Biopsy - methods Breast - diagnostic imaging Color Deep learning Diagnosis, Computer-Assisted - methods Diagnostic systems Digital histopathology Histological Techniques Histopathology Humans Image processing Image Processing, Computer-Assisted - methods Internal Medicine Machine Learning Other Pathology, Clinical - methods Stain Normalization Staining and Labeling - methods Standardization Digital histopathology Deep learning Stain Normalization Image processing
ISSN:	0895-6111, 1879-0771, 1879-0771
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Abstract	•Digital histopathology slides have many sources of variance.•These variances can cause algorithms to perform erratically.•Stain Normalization using Sparse AutoEncoders (StaNoSA) in introduced.•It standardizes color distributions of a test image to a single template image.•Validated using three experiments with five other color standardization approaches. 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.
AbstractList	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. •Digital histopathology slides have many sources of variance.•These variances can cause algorithms to perform erratically.•Stain Normalization using Sparse AutoEncoders (StaNoSA) in introduced.•It standardizes color distributions of a test image to a single template image.•Validated using three experiments with five other color standardization approaches. 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. 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.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. Highlights•Digital histopathology slides have many sources of variance. •These variances can cause algorithms to perform erratically. •Stain Normalization using Sparse AutoEncoders (StaNoSA) in introduced. •It standardizes color distributions of a test image to a single template image. •Validated using three experiments with five other color standardization approaches.
Author	Madabhushi, Anant Janowczyk, Andrew Basavanhally, Ajay
AuthorAffiliation	1 Case Western Reserve University, Cleveland, Ohio 2 Inspirata, Inc., Tampa, Florida
AuthorAffiliation_xml	– name: 1 Case Western Reserve University, Cleveland, Ohio – name: 2 Inspirata, Inc., Tampa, Florida
Author_xml	– sequence: 1 givenname: Andrew surname: Janowczyk fullname: Janowczyk, Andrew email: andrew.janowczyk@case.edu organization: Case Western Reserve University, Cleveland, OH, United States – sequence: 2 givenname: Ajay surname: Basavanhally fullname: Basavanhally, Ajay organization: Inspirata, Inc., Tampa, FL, United States – sequence: 3 givenname: Anant surname: Madabhushi fullname: Madabhushi, Anant organization: Case Western Reserve University, Cleveland, OH, United States
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/27373749$$D View this record in MEDLINE/PubMed
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Keywords	Digital histopathology Deep learning Stain Normalization Image processing
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Snippet	•Digital histopathology slides have many sources of variance.•These variances can cause algorithms to perform erratically.•Stain Normalization using Sparse... Highlights•Digital histopathology slides have many sources of variance. •These variances can cause algorithms to perform erratically. •Stain Normalization... Digital histopathology slides have many sources of variance, and while pathologists typically do not struggle with them, computer aided diagnostic algorithms...
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SubjectTerms	Algorithms Biopsy - methods Breast - diagnostic imaging Color Deep learning Diagnosis, Computer-Assisted - methods Diagnostic systems Digital histopathology Histological Techniques Histopathology Humans Image processing Image Processing, Computer-Assisted - methods Internal Medicine Machine Learning Other Pathology, Clinical - methods Stain Normalization Staining and Labeling - methods Standardization
Title	Stain Normalization using Sparse AutoEncoders (StaNoSA): Application to digital pathology
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