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

Final Report for 2015 ER&L + EBSCO Library Fellowship Research Project

We report findings from a comprehensive assessment of e-book user experience (search and information seeking) from transaction logs, e-book usage data, and user tests. There are differences between e-book and general searches in terms of query length, number of queries and actions per session. There are also distinctive reading patterns from e-book usage data. The user tests showed that experience levels with e-books and features of e-book platforms influenced users’ information seeking behavior. Results of the assessment have significant implications for the design of e-book features to support users’ reading strategies and help libraries create a consistent e-book user experience

Entanglement criterion via general symmetric informationally complete measurements

We study the quantum separability problem by using general symmetric informationally complete measurements and present a separability criterion for arbitrary dimensional bipartite systems. We show by detailed examples that our criterion is more powerful than the existing ones in entanglement detection.Comment: 8 pages, 5 figure