2019
DOI: 10.3389/fmed.2019.00193
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A High-Performance System for Robust Stain Normalization of Whole-Slide Images in Histopathology

Abstract: Stain normalization is an important processing task for computer-aided diagnosis (CAD) systems in modern digital pathology. This task reduces the color and intensity variations present in stained images from different laboratories. Consequently, stain normalization typically increases the prediction accuracy of CAD systems. However, there are computational challenges that this normalization step must overcome, especially for real-time applications: the memory and run-time bottlenecks associated with the proces… Show more

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Cited by 78 publications
(49 citation statements)
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“…To overcome the staining inconsistency of histology slides, multiple researchers have applied operations to standardize specimen colors in histopathological images prior to analysis ( Ranefall et al, 1997 ; Reinhard et al, 2001 ; Macenko et al, 2009 ; Tam et al, 2016 ; Vahadane et al, 2016 ; Anghel et al, 2019 ; Ren et al, 2019 ). One common approach to tackle stain normalization issue is to extract multiple affinities for specific biological substances, and then perform some kind of projection from a preselected reference image to all images.…”
Section: Methodsmentioning
confidence: 99%
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“…To overcome the staining inconsistency of histology slides, multiple researchers have applied operations to standardize specimen colors in histopathological images prior to analysis ( Ranefall et al, 1997 ; Reinhard et al, 2001 ; Macenko et al, 2009 ; Tam et al, 2016 ; Vahadane et al, 2016 ; Anghel et al, 2019 ; Ren et al, 2019 ). One common approach to tackle stain normalization issue is to extract multiple affinities for specific biological substances, and then perform some kind of projection from a preselected reference image to all images.…”
Section: Methodsmentioning
confidence: 99%
“…One common approach to tackle stain normalization issue is to extract multiple affinities for specific biological substances, and then perform some kind of projection from a preselected reference image to all images. Specifically, color deconvolution methods ( Macenko et al, 2009 ; Vahadane et al, 2016 ; Anghel et al, 2019 ) have been utilized extensively in the past decades by transforming the original RGB image into other color space like Lab ( Reinhard et al, 2001 ) and extract the stain vectors. Unsupervised vector estimation methods ( Anghel et al, 2019 ) and generative methods ( Ren et al, 2019 ) have also emerged in the past years.…”
Section: Methodsmentioning
confidence: 99%
“…Color augmentation (Figure 1), where the color channels of images are altered at random during training to prevent a model from learning stain characteristics of a specific site have also been utilized in histology deep learning tasks 28,29 . Most assessments of stain normalization and augmentation techniques have focused on the performance of models in validation sets, rather than true elimination of batch effect 30,31 . Here, we describe the clinical and slide level variability between sites in TCGA, and methods to ensure robust use of internal and external validation to minimize false positive findings with deep learning image analysis.…”
Section: Mainmentioning
confidence: 99%
“…Finally, stain normalization and color augmentation techniques should still be used to improve model accuracy in external validation and implementation. Although normalization and augmentation do not completely prevent models from learning site specific characteristics, several studies have reported greater validation accuracies with the use of such techniques 30,31 . It is likely that these techniques eliminate some but not all of the reliance that deep learning models have on stain related features; by making the differences in slide characteristics more subtle, models may be more likely to pick up on biologically relevant factors.…”
Section: Best Practices For Addressing Batch Effect For Deep Learningmentioning
confidence: 99%
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