NDLI: Removing Batch Effects From Histopathological Images for Enhanced Cancer Diagnosis

Content Provider	IEEE Xplore Digital Library
Author	Kothari, S. Phan, J.H. Stokes, T.H. Osunkoya, A.O. Young, A.N. Wang, M.D.
Copyright Year	2013
Abstract	Researchers have developed computer-aided decision support systems for translational medicine that aim to objectively and efficiently diagnose cancer using histopathological images. However, the performance of such systems is confounded by nonbiological experimental variations or “batch effects” that can commonly occur in histopathological data, especially when images are acquired using different imaging devices and patient samples. This is even more problematic in large-scale studies in which cross-laboratory sharing of large volumes of data is necessary. Batch effects can change quantitative morphological image features and decrease the prediction performance. Using four batches of renal tumor images, we compare one image-level and five feature-level batch effect removal methods. Principal component variation analysis shows that batch is a large source of variance in image features. Results show that feature-level normalization methods reduce batch-contributed variance to almost zero. Moreover, feature-level normalization, especially ComBatN, improves cross-batch and combined-batch prediction performance. Compared to no normalization, ComBatN improves performance in 83% and 90% of cross-batch and combined-batch prediction models, respectively.
Page Count	8
File Size	1360336
Starting Page	765
Ending Page	772
File Format	PDF
ISSN	21682194
Volume Number	18
Issue Number	3
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image color analysis Image segmentation Feature extraction Cancer Tumors Predictive models Educational institutions pathology Biomedical informatics decision support systems image representation
Content Type	Text
Resource Type	Article
Subject	Health Information Management Health Informatics Electrical and Electronic Engineering Computer Science Applications Biotechnology

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