AC-MLP: Axial Convolution-MLP Mixer for nuclei segmentation in histopathological images
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Recent MLP-Mixer has a good ability to handle long-range dependencies, however, to have a good performance, one requires huge data and expensive infrastructures for the pre-training process. In this study, we proposed a novel model for nuclei image segmentation namely Axial Convolutional-MLP Mixer, by replacing the token mixer of MLP-Mixer with a new operator, Axial Convolutional Token Mix. Specifically, in the Axial Convolutional Token Mix, we inherited the idea of axial depthwise convolution to create a flexible receptive field. We also proposed a Long-range Attention module that uses dilated convolution to extend the convolutional kernel size, thereby addressing the issue of long-range dependencies. Experiments demonstrate that our model can achieve high results on small medical datasets, with Dice scores of 90.20% on the GlaS dataset, 80.43% on the MoNuSeg dataset, and without pre-training. The code will be available at https://github.com/thanhthu152/AC-MLP.
Tài liệu tham khảo
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Thu, N. T., D. B. Duong, T. T. Thao, và P. V. Truong. “AC-MLP: Axial Convolution-MLP Mixer for Nuclei Segmentation in Histopathological Images”. Tạp Chí Khoa học Và Công nghệ - Đại học Đà Nẵng, vol 22, số p.h 12, Tháng Chạp 2024, tr 64-69, doi:10.31130/ud-jst.2024.332E.
