PDNet: an advanced architecture for polyp image segmentation

Hanqing Liu; Zhipeng Zhao; Ruichun Tang; Peishun Liu; Yixin Chen; Jianjun Zhang; Jing Jia

doi:10.1117/12.2643392

12 October 2022 PDNet: an advanced architecture for polyp image segmentation

Hanqing Liu, Zhipeng Zhao, Ruichun Tang, Peishun Liu, Yixin Chen, Jianjun Zhang, Jing Jia

Proceedings Volume 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022); 123421H (2022) https://doi.org/10.1117/12.2643392
Event: Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 2022, Wuhan, China

Abstract

In order to improve the segmentation accuracy of polyp image segmentation under colonoscopy, we propose PVT Dual-Upsampling Net (PDNet). PDNet adopts the encoder network based on Transformer as the backbone network for downsampling, and designs a dual upsampling module based on cascaded fusion network and simple connection network to recover the loss of high-level image features caused by the downsampling process, and obtains a high-level semantic feature map with the same resolution as the input image. The multi-feature fusion module is used to aggregate the low-level feature map and high-level semantic feature map. We validate the model on three publicly available datasets, and our experimental evaluations show that the suggested architecture produces good segmentation results on datasets.

Citation Download Citation

Hanqing Liu, Zhipeng Zhao, Ruichun Tang, Peishun Liu, Yixin Chen, Jianjun Zhang, and Jing Jia "PDNet: an advanced architecture for polyp image segmentation", Proc. SPIE 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 123421H (12 October 2022); https://doi.org/10.1117/12.2643392

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