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- W2910628442 abstract "In this paper, we propose a pyramid network structure to improve the FCN-based segmentation solutions and apply it to label thyroid follicles in histology images. Our design is based on the notion that a hierarchical updating scheme, if properly implemented, can help FCNs capture the major objects, as well as structure details in an image. To this end, we devise a residual module to be mounted on consecutive network layers, through which pixel labels would be propagated from the coarsest layer towards the finest layer in a bottom-up fashion. We add five residual units along the decoding path of a modified U-Net to make our segmentation network, Res-Seg-Net. Experiments demonstrate that the multi-resolution set-up in our model is effective in producing segmentations with improved accuracy and robustness." @default.
- W2910628442 created "2019-01-25" @default.
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- W2910628442 date "2019-01-11" @default.
- W2910628442 modified "2023-09-26" @default.
- W2910628442 title "Residual Pyramid FCN for Robust Follicle Segmentation" @default.
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- W2910628442 doi "https://doi.org/10.48550/arxiv.1901.03760" @default.
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