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Encoding feature supervised UNet++: Redesigning Supervision for liver and tumor segmentation. (arXiv:2211.08146v1 [eess.IV])
Nov. 16, 2022, 2:15 a.m. | Jiahao Cui, Ruoxin Xiao (co-first author), Shiyuan Fang, Minnan Pei, Yixuan Yu
cs.CV updates on arXiv.org arxiv.org
Liver tumor segmentation in CT images is a critical step in the diagnosis,
surgical planning and postoperative evaluation of liver disease. An automatic
liver and tumor segmentation method can greatly relieve physicians of the heavy
workload of examining CT images and better improve the accuracy of diagnosis.
In the last few decades, many modifications based on U-Net model have been
proposed in the literature. However, there are relatively few improvements for
the advanced UNet++ model. In our paper, we propose …
More from arxiv.org / cs.CV updates on arXiv.org
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