方法证据记录
U-Net
U-Net is a fully convolutional encoder-decoder architecture, introduced by Ronneberger, Fischer, and Brox at MICCAI 2015, that produces dense pixel-wise segmentation masks by combining a contracting path that captures context with a symmetric expanding path that enables precise localization — all bridged by skip connections that preserve fine spatial detail. It established the standard baseline for biomedical image segmentation and has since become one of the most widely adopted architectures for any pixel-level prediction task.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
U-Net: Convolutional Networks for Biomedical Image Segmentation
分类方法记录 · ml-model / deep-learning
- 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
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