Enhanced pooling-convolution for pathological image multi-class segmentation

Hao Wu; Xinjian Chen; Weifang Zhu; Fei Shi; Dehui Xiang

doi:10.1117/12.2651219

6 April 2023 Enhanced pooling-convolution for pathological image multi-class segmentation

Hao Wu, Xinjian Chen, Weifang Zhu, Fei Shi, Dehui Xiang

Proceedings Volume 12471, Medical Imaging 2023: Digital and Computational Pathology; 124711W (2023) https://doi.org/10.1117/12.2651219
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

Recent studies have achieved a great success in medical image segmentation, but do not perform well in the application of pathological image segmentation. In traditional segmentation networks, some important features may be lost during the encoding process. In this paper, an Enhanced Pooling-Convolution (EPC) module is proposed to add weights to the space and channels of features in the encoding process. EPC evaluates the differences and complementarities of features between max pooling, average pooling, and convolution in the pooling process. Channel based attention is further used to weight different channels. VGG16 is used as the backbone in the U-shaped network, and the number of channels for upsampling is reduced during decoding process. It shows that the pooling and convolution block with three consecutive convolution layers can be replaced with the EPC module. Experimental results shows that the average DICE coefficient of our method is 2.55% higher than that of U-Net.

Citation Download Citation

Hao Wu, Xinjian Chen, Weifang Zhu, Fei Shi, and Dehui Xiang "Enhanced pooling-convolution for pathological image multi-class segmentation", Proc. SPIE 12471, Medical Imaging 2023: Digital and Computational Pathology, 124711W (6 April 2023); https://doi.org/10.1117/12.2651219

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