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6 July 2018 Deep learning and color variability in breast cancer histopathological images: a preliminary study
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Proceedings Volume 10718, 14th International Workshop on Breast Imaging (IWBI 2018); 107181E (2018) https://doi.org/10.1117/12.2316613
Event: The Fourteenth International Workshop on Breast Imaging, 2018, Atlanta, Georgia, United States
Abstract
Variability in the color appearance in H and E stained histopathological images are typically observed. Color normalization has been found useful in standardizing the color appearance of H and E stained histopathological images prior to quantitative analysis with machine learning (using handcrafted features). However, its usefulness has not been previously studied when deep convolutional neural networks (CNNs) are used in classifying H and E stained breast cancer histopathological images. In this paper, we have adopted a representative CNN for classifying breast cancer histopathological images and evaluated the benefit/necessity of color normalisation using the commonly used Macenko, Khan and Reinhard color normalization methods. The representative CNN was implemented in-house and was verified. The BreaKHis dataset was used to train and test the CNN model. The preliminary results did not show significant superiority in the CNN performance when color normalization was used to standardize the color appearance of histopathological image. Furthermore, the classification performance of a magnification-independent CNN is comparable to that of magnification-specific CNNs with an additional benefit of a simpler classification scheme and training for only one CNN models (rather than multiple magnificationspecific models). It may also have an advantage in clinical practice when the magnification factor of a histopathological image is not known.
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Gobert Lee, Mariusz Bajger, and Kevin Clark "Deep learning and color variability in breast cancer histopathological images: a preliminary study", Proc. SPIE 10718, 14th International Workshop on Breast Imaging (IWBI 2018), 107181E (6 July 2018); https://doi.org/10.1117/12.2316613
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