Normalization in Training U-Net for 2-D Biomedical Semantic Segmentation

Volume: 4, Issue: 2, Pages: 1792 - 1799
Published: Apr 1, 2019
Abstract
2D biomedical semantic segmentation is important for robotic vision in surgery. Segmentation methods based on Deep Convolutional Neural Network (DCNN) can out-perform conventional methods in terms of both accuracy and levels of automation. One common issue in training a DCNN for biomedical semantic segmentation is the internal covariate shift where the training of convolutional kernels is encumbered by the distribution change of input features,...
Paper Details
Title
Normalization in Training U-Net for 2-D Biomedical Semantic Segmentation
Published Date
Apr 1, 2019
Volume
4
Issue
2
Pages
1792 - 1799
Citation AnalysisPro
  • Scinapse’s Top 10 Citation Journals & Affiliations graph reveals the quality and authenticity of citations received by a paper.
  • Discover whether citations have been inflated due to self-citations, or if citations include institutional bias.