An Efficient Approach for Polyps Detection in Endoscopic Videos Based on Faster R-CNN

Polyp has long been considered as one of the major etiologies to colorectal cancer which is a fatal disease around the world, thus early detection and recognition of polyps plays a crucial role in clinical routines. Accurate diagnoses of polyps through endoscopes operated by physicians becomes a challenging task not only due to the varying expertise of physicians, but also the inherent nature of endoscopic inspections. To facilitate this process, computer-aid techniques that emphasize fully-conventional image processing and novel machine learning enhanced approaches have been dedicatedly designed for polyp detection in endoscopic videos or images. Among all proposed algorithms, deep learning based methods take the lead in terms of multiple metrics in evolutions for algorithmic performance. In this work, a highly effective model, namely the faster region-based convolutional neural network (Faster R-CNN) is implemented for polyp detection. In comparison with the reported results of the state-of-the-art approaches on polyps detection, extensive experiments demonstrate that the Faster R-CNN achieves very competing results, and it is an efficient approach for clinical practice.Comment: 6 pages, 10 figures,2018 International Conference on Pattern Recognitio

Sparse integrative clustering of multiple omics data sets

High resolution microarrays and second-generation sequencing platforms are powerful tools to investigate genome-wide alterations in DNA copy number, methylation and gene expression associated with a disease. An integrated genomic profiling approach measures multiple omics data types simultaneously in the same set of biological samples. Such approach renders an integrated data resolution that would not be available with any single data type. In this study, we use penalized latent variable regression methods for joint modeling of multiple omics data types to identify common latent variables that can be used to cluster patient samples into biologically and clinically relevant disease subtypes. We consider lasso [J. Roy. Statist. Soc. Ser. B 58 (1996) 267-288], elastic net [J. R. Stat. Soc. Ser. B Stat. Methodol. 67 (2005) 301-320] and fused lasso [J. R. Stat. Soc. Ser. B Stat. Methodol. 67 (2005) 91-108] methods to induce sparsity in the coefficient vectors, revealing important genomic features that have significant contributions to the latent variables. An iterative ridge regression is used to compute the sparse coefficient vectors. In model selection, a uniform design [Monographs on Statistics and Applied Probability (1994) Chapman & Hall] is used to seek "experimental" points that scattered uniformly across the search domain for efficient sampling of tuning parameter combinations. We compared our method to sparse singular value decomposition (SVD) and penalized Gaussian mixture model (GMM) using both real and simulated data sets. The proposed method is applied to integrate genomic, epigenomic and transcriptomic data for subtype analysis in breast and lung cancer data sets.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS578 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

The clustering of galaxies with pseudo bulge and classical bulge in the local Universe

We investigate the clustering properties and close neighbour counts for galaxies with different types of bulges and stellar masses. We select samples of "classical" and "pseudo" bulges, as well as "bulge-less" disk galaxies, based on the bulge/disk decomposition catalog of SDSS galaxies provided by Simard et al. (2011). For a given galaxy sample we estimate: the projected two-point cross-correlation function with respect to a spectroscopic reference sample, w_p(r_p), and the average background-subtracted neighbour count within a projected separation using a photometric reference sample, N_neighbour(<r_p). We compare the results with the measurements of control samples matched in color, concentration and redshift. We find that, when limited to a certain stellar mass range and matched in color and concentration, all the samples present similar clustering amplitudes and neighbour counts on scales above ~0.1h^{-1}Mpc. This indicates that neither the presence of a central bulge, nor the bulge type is related to intermediate-to-large scale environments. On smaller scales, in contrast, pseudo-bulge and pure-disk galaxies similarly show strong excess in close neighbour count when compared to control galaxies, at all masses probed. For classical bulges, small-scale excess is also observed but only for M_stars < 10^{10} M_sun; at higher masses, their neighbour counts are similar to that of control galaxies at all scales. These results imply strong connections between galactic bulges and galaxy-galaxy interactions in the local Universe, although it is unclear how they are physically linked in the current theory of galaxy formation.Comment: 14 pages, 16 figures, accepted for publication in MNRA