Blood vessel segmentation methods based on deep neural networks have achieved satisfactory results. However, these methods are usually supervised learning methods, which require large numbers of retinal images with high quality pixel-level ground-truth labels. In practice, the task of labeling these retinal images is very costly, financially and in human effort. To deal with these problems, we propose a semi-supervised learning method which can be used in blood vessel segmentation with limited labeled data. In this method, we use the improved U-Net deep learning network to segment the blood vessel tree. On this basis, we implement the U-Net network-based training dataset updating strategy. A large number of experiments are presented to analyze the segmentation performance of the proposed semi-supervised learning method. The experiment results demonstrate that the proposed methodology is able to avoid the problems of insufficient hand-labels, and achieve satisfactory performance.
Symmetry Worksheets
Symmetry Worksheets
On refined enumerations of some symmetry classes of ASMs : A. V. Razumov : Free Download, Borrow, and Streaming : Internet Archive
PDF) Basket-Handle Arch and Its Optimum Symmetry Generation as a Structural Element and Keeping the Aesthetic Point of View
ALL MY SYMMETRY PRODUCTS! 3 levels of difficulty for differenciation. No prep, just print, copy and use! For the level 1: the drawing is complete and
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Symmetry & Parity Workbook. FREE 80 page add-on for all purchasers of the book.
Symmetry An Open Access Journal from MDPI
Symmetry, Free Full-Text
Symmetry, Free Full-Text, astd meta
Seeing Symmetry: Math Horizons: Vol 30, No 1
Symmetry, Free Full-Text
Symmetry, Free Full-Text
