  Histopathology has played an essential role in cancer diagnosis. With the
rapid advances in convolutional neural networks (CNN). Various CNN-based
automated pathological image segmentation approaches have been developed in
computer-assisted pathological image analysis. In the past few years,
Transformer neural networks (Transformer) have shown the unique merit of
capturing the global long-distance dependencies across the entire image as a
new deep learning paradigm. Such merit is appealing for exploring spatially
heterogeneous pathological images. However, there have been very few, if any,
studies that have systematically evaluated the current Transformer-based
approaches in pathological image segmentation. To assess the performance of
Transformer segmentation models on whole slide images (WSI), we quantitatively
evaluated six prevalent transformer-based models on tumor segmentation, using
the widely used PAIP liver histopathological dataset. For a more comprehensive
analysis, we also compare the transformer-based models with six major
traditional CNN-based models. The results show that the Transformer-based
models exhibit a general superior performance over the CNN-based models. In
particular, Segmenter, Swin-Transformer and TransUNet-all
transformer-based-came out as the best performers among the twelve evaluated
models.
