期刊
COMPUTERIZED MEDICAL IMAGING AND GRAPHICS
卷 57, 期 -, 页码 50-61出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compmedimag.2016.05.003
关键词
Digital histopathology; Stain Normalization; Deep learning; Image processing
资金
- National Cancer Institute of the National Institutes of Health [1U24CA199374-01, R21CA167811-01, R21CA179327-01]
- National Institute of Diabetes and Digestive and Kidney Diseases [R01 DK098503-02, R21CA195152-01]
- DOD Prostate Cancer Synergistic Idea Development Award [PC120857]
- DOD Lung Cancer Idea Development New Investigator Award [LC130463]
- DOD Prostate Cancer Idea Development Award
- Ohio Third Frontier Technology development Grant
- CTSC Coulter Annual Pilot Grant
- Case Comprehensive Cancer Center Pilot Grant VelaSano Grant from the Cleveland Clinic the Wallace H. Coulter Foundation Program in the Department of Biomedical Engineering at Case Western Reserve University
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 (StalloSA) 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. StalloSA was validated on three experiments and compared against five other color standardization approaches and shown to have either comparable or superior results. (C) 2016 Elsevier Ltd. All rights reserved.
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