Machine learning
U-Net
U-Net是一种全卷积编码器-解码器架构，由Ronneberger、Fischer和Brox在2015年的MICCAI会议上提出，它通过结合捕获上下文的收缩路径和实现精确局部化的对称扩张路径，并利用跳跃连接保留精细的空间细节，来生成密集的像素级分割掩码。它确立了生物医学图像分割的标准基线，并已成为任何像素级预测任务中最广泛采用的架构之一。
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来源
- Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. In N. Navab et al. (Eds.), Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, LNCS 9351 (pp. 234–241). Springer. DOI: 10.1007/978-3-319-24574-4_28 ↗
- Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning (Ch. 9: Convolutional Networks). MIT Press. ISBN: 978-0-262-03561-3
如何引用本页
ScholarGate. (2026, June 3). U-Net: Convolutional Networks for Biomedical Image Segmentation. ScholarGate. https://scholargate.app/zh/deep-learning/u-net
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