Image annotation, or prediction of multiple tags for an image, is a challenging task. Most current algorithms are based on large sets of handcrafted features. Deep convolutional neural networks have recently outperformed humans in image classification, and these networks can be used to extract features highly predictive of an image's tags. In this study, we analyze semantic information in features derived from two pre-trained deep network classifiers by evaluating their performance in nearest neighbor-based approaches to tag prediction. We generally exceed performance of the manual features when using the deep features. We also find complementary information in the manual and deep features when used in combination for image annotation.

MOTIVATION: To advance understanding of eukaryotic cell division, it is important to observe the process precisely. To this end, researchers monitor changes in dividing cells as they traverse the cell cycle, with the presence or absence of morphological or genetic markers indicating a cell's position in a particular interval of the cell cycle. A wide variety of marker data is available, including information-rich cellular imaging data. However, few formal statistical methods have been developed to use these valuable data sources in estimating how a population of cells progresses through the cell cycle. Furthermore, existing methods are designed to handle only a single binary marker of cell cycle progression at a time. Consequently, they cannot facilitate comparison of experiments involving different sets of markers. RESULTS: Here, we develop a new sampling model to accommodate an arbitrary number of different binary markers that characterize the progression of a population of dividing cells along a branching process. We engineer a strain of Saccharomyces cerevisiae with fluorescently labeled markers of cell cycle progression, and apply our new model to two image datasets we collected from the strain, as well as an independent dataset of different markers. We use our model to estimate the duration of post-cytokinetic attachment between a S.cerevisiae mother and daughter cell. The Java implementation is fast and extensible, and includes a graphical user interface. Our model provides a powerful and flexible cell cycle analysis tool, suitable to any type or combination of binary markers. AVAILABILITY: The software is available from: http://www.cs.duke.edu/~amink/software/cloccs/. CONTACT: [email protected]; [email protected].