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. 2022 Apr 27;13:862702. doi: 10.3389/fimmu.2022.862702

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Copyright © 2022 Liu, Huang, Sun, Li, Luo, Cui, Zhu, Bi, Chen and Liu

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Recognition effect of the Unet network enhanced by full convolutional attention. (A) Input image is the collected HE original image; Ground truth is the output result of manually annotated images; Result is the Unet network output Result of full convolutional attention enhancement. (B) Background is the Background area outside the target area, and CELL is the ganglion CELL, INL is the kernel layer, IPL is the internal layer, OPL is the external network layer, and RNFL is the nerve fiber layer. PA is pixel accuracy, and IOU is intersection ratio parameter. (C) The segmentation result of corrosion calculation was added to the original algorithm. (D) Segmentation results obtained by the original algorithm.