Quantitative Analysis of Differentiation Activity for Mouse Embryonic Stem Cells by Deep Learning for Cell Center Detection using Three-Dimensional Confocal Fluorescence Microscopy Images.

Accurate single cell segmentation provides means to monitor the behavior of single cell within a population of cells. Time-lapse fluorescence images are used to reveal heterogeneous nature of single mouse embryonic stem cell (ESC) colony and monitor fluctuations of the cell states. Automatic quantification of speed and status shifts of the ESCs depends on accurate single cell segmentation that is used to calculate the 3D center of every cell and track this cell for the quantification.

Deep learning based real-time tourist spots detection and recognition mechanism.

More and more information on tourist spots is being represented as pictures rather than text. Consequently, tourists who are interested in a specific attraction shown in pictures may have no idea how to perform a text search to get more information about the interesting tourist spots. In the view of this problem and to enhance the competitiveness of the tourism market, this research proposes an innovative tourist spot identification mechanism, which is based on deep learning-based object detection technology, for real-time detection and identification of tourist spots by taking pictures on location or retrieving images from the Internet.