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Histopathology has played an essential role in cancer diagnosis. With therapid advances in convolutional neural networks (CNN). Various CNN-basedautomated pathological image segmentation approaches have been developed incomputer-assisted pathological image analysis. In the past few years,Transformer neural networks (Transformer) have shown the unique merit ofcapturing the global long-distance dependencies across the entire image as anew deep learning paradigm. Such merit is appealing for exploring spatiallyheterogeneous pathological images. However, there have been very few, if any,studies that have systematically evaluated the current Transformer-basedapproaches in pathological image segmentation. To assess the performance ofTransformer segmentation models on whole slide images (WSI), we quantitativelyevaluated six prevalent transformer-based models on tumor segmentation, usingthe widely used PAIP liver histopathological dataset. For a more comprehensiveanalysis, we also compare the transformer-based models with six majortraditional CNN-based models. The results show that the Transformer-basedmodels exhibit a general superior performance over the CNN-based models. Inparticular, Segmenter, Swin-Transformer and TransUNet-alltransformer-based-came out as the best performers among the twelve evaluatedmodels.

Evaluating Transformer-based Semantic Segmentation Networks for Pathological Image Segmentation