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Recent advances in semantic image segmentation have helped researchers understand an image by distinguishing different objects and understanding their relationships. Semantic image segmentation algorithms have been effectively used to identify the characteristics of complex types of tissue cells in histopathological slide images. Still, the standardization of colors has been one of the major challenges to proceeding with semantic image segmentation algorithms due to the color variation in the histopathological slide images. In this paper, we perform a two-way analysis of color normalization, evaluating four representative color normalization methods with six evaluation metrics on 19 tissue types and reducing dimensions for visualization. The experiment results show that Reinhard's color normalization outperforms other color normalization methods regarding the six evaluation metrics. Additionally, we compared the experiment results based on the color normalization methods and the tissue types using a dimensionality reduction technique. The additional experiment results demonstrate that the types of tissue images are not directly related to the color normalization results, but the dimensionality reduction technique is effective to split different color normalization methods. 

Deep learning algorithms have been successfully adopted to extract meaningful information from digital images, yet many of them have been untapped in the semantic image segmentation of histopathology images. In this paper, we propose a deep convolutional neural network model that strengthens Atrous separable convolutions with a high rate within spatial pyramid pooling for histopathology image segmentation. A well-known model called DeepLabV3Plus was used for the encoder and decoder process. ResNet50 was adopted for the encoder block of the model which provides us the advantage of attenuating the problem of the increased depth of the network by using skip connections. Three Atrous separable convolutions with higher rates were added to the existing Atrous separable convolutions. We conducted a performance evaluation on three tissue types: tumor, tumor-infiltrating lymphocytes, and stroma for comparing the proposed model with the eight state-of-the-art deep learning models: DeepLabV3, DeepLabV3Plus, LinkNet, MANet, PAN, PSPnet, UNet, and UNet++. The performance results show that the proposed model outperforms the eight models on mIOU (0.8058/0.7792) and FSCR (0.8525/0.8328) for both tumor and tumor-infiltrating lymphocytes. 

Digital pathology has played a key role in replacing glass slides with digital images, enhancing various pathology workflows. Whole slide images are digitized pathological images improving the capabilities of digital pathology and contributing to the overall turnaround time for diagnoses. The digitized images have been successfully integrated with artificial intelligence algorithms assisting pathologists in many tasks, but there are still demands to develop a new algorithm for a better diagnosis process. In this paper, we propose a new deep convolutional neural network model integrating a feature pyramid network with a self-attention mechanism in three pathways: encoder, decoder, and self-attention nested for providing accurate tumor region segmentation on whole slide images. The encoder pathway adopts ResNet50 architecture for the bottom-up network. The decoder pathway adopts the feature pyramid network for the top-down network. The self-attention nested pathway forms the attention map represented by the distribution of attention scores focusing on localizing tumor regions and avoiding irrelevant information. The results of our experiment show that the proposed model outperforms the state-of-the-art deep convolutional neural network models in terms of tumor and stromal region segmentation. Moreover, various encoder networks were equipped with the proposed model and compared with each other. The results indicate that the ResNet series using the proposed model outperforms other encoder networks.