建设公司网站账务处理,公司展厅装修,建设银行宁夏分行网站,下载官方版微信论文原文#xff1a;U-Net: Convolutional Networks for Biomedical Image Segmentation (arxiv.org)
英文是纯手打的#xff01;论文原文的summarizing and paraphrasing。可能会出现难以避免的拼写错误和语法错误#xff0c;若有发现欢迎评论指正#xff01;文章偏向于笔…
论文原文U-Net: Convolutional Networks for Biomedical Image Segmentation (arxiv.org)
英文是纯手打的论文原文的summarizing and paraphrasing。可能会出现难以避免的拼写错误和语法错误若有发现欢迎评论指正文章偏向于笔记谨慎食用 1. 原文逐段精读
1.1. Abstract ①Reasonable use of annotation samples ②The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization ③This model is for segmenting neuronal structures in electron microscopic stacks ④This model peforms great in small training sample 1.2. Introduction ①The expectations for machine learning and deep learning in medicine often lie not in classification accuracy, but in region segmentation and other aspects ②They consider the sliding-window model by Ciresan et al. as slow in training and inaccuracy brought by maxpooling ③⭐U-Net takes upsampling instead of pooling ④什么重叠贴图策略我没能明白为啥这样就能预测 ⑤They use elastic deformations to augment there data, which keeps the invariance 1.3. Network Architecture ①The whole framework: ②3*3 convolutions include no padding ③Stride of maxpooling is 2 ④Double the number of channels when downsampling ⑤Up-conv 2*2 halves the number of feature channels 1.4. Training
1.4.1. Data Augmentation
1.5. Experiments
1.6. Conclusion 2. 代码 3. Reference List
Ronneberger, O., Fischer, P. Brox, T. (2015) U-Net: Convolutional Networks for Biomedical Image Segmentation, MICCAI 2015: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, pp 234–241. doi: U-Net: Convolutional Networks for Biomedical Image Segmentation | SpringerLink
