Evaluating transformer-based semantic segmentation networks for pathological image segmentation

Cam Nguyen; Zuhayr Asad; Ruining Deng; Yuankai Huo

doi:10.1117/12.2611177

4 April 2022 Evaluating transformer-based semantic segmentation networks for pathological image segmentation

Cam Nguyen, Zuhayr Asad, Ruining Deng, Yuankai Huo

Proceedings Volume 12032, Medical Imaging 2022: Image Processing; 120323N (2022) https://doi.org/10.1117/12.2611177
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

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.

Conference Presentation

Citation Download Citation

Cam Nguyen, Zuhayr Asad, Ruining Deng, and Yuankai Huo "Evaluating transformer-based semantic segmentation networks for pathological image segmentation", Proc. SPIE 12032, Medical Imaging 2022: Image Processing, 120323N (4 April 2022); https://doi.org/10.1117/12.2611177

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