ArticleSheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi2022
[Exploring and analyzing the improvement mechanism of U-Net and its application in medical image segmentation].
Article in Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- SMoFFI-SegFormer: a novel approach for ovarian tumor segmentation based on an improved SegFormer architecture.Frontiers in oncology · 2025Article
- Deep Learning for Medical Image-Based Cancer Diagnosis.Cancers · 2023Review
- [Medical image instance segmentation: from candidate region to no candidate region].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2022Article
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Authors and funding
6 authors.
Funding
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Abstract
Remarkable results have been realized by the U-Net network in the task of medical image segmentation. In recent years, many scholars have been researching the network and expanding its structure, such as improvement of encoder and decoder and improvement of skip connection. Based on the optimization of U-Net structure and its medical image segmentation techniques, this paper elucidates in the following: First, the paper elaborates on the application of U-Net in the field of medical image segmentation; Then, the paper summarizes the seven improvement mechanism of U-Net: dense connection mechanism, residual connection mechanism, multi-scale mechanism, ensemble mechanism, dilated mechanism, attention mechanism, and transformer mechanism; Finally, the paper states the ideas and methods on the U-Net structure improvement in a bid to provide a reference for later researches, which plays a significant part in advancing U-Net.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.