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In Frontiers in bioengineering and biotechnology ; h5-index 0.0

Image analysis tools for cancer, such as automatic nuclei segmentation, are impacted by the inherent variation contained in pathology image data. Convolutional neural networks (CNN), demonstrate success in generalizing to variable data, illustrating great potential as a solution to the problem of data variability. In some CNN-based segmentation works for digital pathology, authors apply color normalization (CN) to reduce color variability of data as a preprocessing step prior to prediction, while others do not. Both approaches achieve reasonable performance and yet, the reasoning for utilizing this step has not been justified. It is therefore important to evaluate the necessity and impact of CN for deep learning frameworks, and its effect on downstream processes. In this paper, we evaluate the effect of popular CN methods on CNN-based nuclei segmentation frameworks.

Pontalba Justin Tyler, Gwynne-Timothy Thomas, David Ephraim, Jakate Kiran, Androutsos Dimitrios, Khademi April


color normalization, computational pathology, deep learning, neural networks, nuclei segmentation, standardization