SD-Layer: Stain deconvolutional layer for CNNs in medical microscopic imaging
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© Springer International Publishing AG 2017. Convolutional Neural Networks (CNNs) are typically trained in the RGB color space. However, in medical imaging, we believe that pixel stain quantities offer a fundamental view of the interaction between tissues and stain chemicals. Since the optical density (OD) colorspace allows to compute pixel stain quantities from pixel RGB intensities using the Beer-Lambert’s law, we propose a stain deconvolutional layer, hereby named as SD-Layer, affixed at the front of CNN that performs two functions: (1) it transforms the input RGB microscopic images to Optical Density (OD) space and (2) this layer deconvolves OD image with the stain basis learned through backpropagation and provides tissue-specific stain absorption quantities as input to the following CNN layers. With the introduction of only nine additional learnable parameters in the proposed SD-Layer, we obtain a considerably improved performance on two standard CNN architectures: AlexNet and T-CNN. Using the T-CNN architecture prefixed with the proposed SD-Layer, we obtain 5-fold cross-validation accuracy of 93.2% in the problem of differentiating malignant immature White Blood Cells (WBCs) from normal immature WBCs for cancer detection.
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