Automatic breast tumor detection in ABVS images based on convolutional neural network and superpixel patterns

Breast cancer is one of the most common female malignancies, as well as the second leading cause of mortality for women. Early detection and treatment can dramatically decrease the mortality rate. Recently, automated breast volume scanner (ABVS) has become one of the most frequently used diagnose methods for breast tumor screening because of its operator-independent and reproducible advantages. However, it is a challenging job to obtain the tumors’ accurate locations and shapes by reviewing hundreds of ABVS slices. In this paper, a novel computer-aided detection (CADe) system is developed to reduce clinicians’ reading time and improve the efficiency. The CADe system mainly contains three parts: tumor candidate acquisition, false-positive reduction and tumor segmentation. Firstly, a local phase-based approach is built to obtain breast tumor candidates for further recognition. Subsequently, a convolutional neural network (CNN) is applied to reduce false positives (FPs). The introduction of CNN can help to avoid complicated feature extraction as well as elevate the accuracy and efficiency. Finally, superpixel-based segmentation is used to outline the breast tumor. Here, superpixel-based local binary pattern (SLBP) is proposed to assist the segmentation, which improves the performance. The methods were evaluated on a clinical ABVS dataset whose abnormal cases were manually labeled by an experienced radiologist. The experiment results were mainly composed of two parts. At the FP reduction stage, the proposed CNN achieved 100% and 78.12% sensitivity with FPs/case of 2.16 and 0. At the segmentation stage, our SLBP obtained 82.34% true positive, 15.79% false positive and 83.59% Dice similarity. In summary, the proposed CADe system demonstrated promising potential to detect and outline breast tumors in ABVS images.

     

Liquid phase sintering and characterization of SiC ceramics

The present study focuses on the sintering of silicon carbide-based ceramics (SiC) by liquid phase sintering (LPS) followed by characterization of the produced ceramics. AlN/Re₂O₃ mixtures were used as additives in the LPS process. In the first step, the LPS-SiC materials were produced in a graphite resistance furnace in the form of discs at different temperatures. The conditions with the best results regarding real density and relative density were taken as reference for sintering in the form of prismatic bars. In the second step, these samples were evaluated regarding fracture toughness (K_IC), by the Single Edge V Notch Beam – SEVNB – method, and flexural strength. K_IC behavior was evaluated according to the depth and curvature radius of the notches. Reliable K_IC values were presented when the ceramic displayed a small curvature radius at the notch tip. When the radius was large, it did not maintain the square root singularity of the notch tip. Tests were carried out to determine K_IC values in atmospheric air and water. K_IC results were lower in water than air, with a decrease ranging between 2.56% and 11.26%. The observations indicated a direct grain size correlation between K_IC values and fracture strength of the SiC ceramics.     

高精度多环片自动装配控制技术

为了实现对微小环片零件的自动化装配,搭建了自动装配系统.通过4根直线导向轴与4个直线轴承来提高系统的导向精度和刚度.采用直线导轨进行各装配作业模块之间的切换,保证了微小环片零件的自动装配与取出.在环片的装配方向上,螺旋升降机和光栅尺实现环片的位置精度控制.在Lab VIEW编程环境中,采用分层软件架构和模块化控制思想,避免了不必要的数据循环检测与丢失,能够达到环片组件的装配精度要求.控制系统分为系统初始化模块、参数设置模块、装配模块和取出模块,自动装配系统通过各个模块间的相互交流配合完成装配任务.采用本文中自动装配系统装配环片的实验结果表明,环片零件装配的最大位置误差为26μm,垂直度误差为17μm,平均装配时间为75 s/片,可满足环片组件所需的精度要求.     

Group sequential crossover trial designs with strong control of the familywise error rate

Crossover designs are an extremely useful tool to investigators, and group sequential methods have proven highly proficient at improving the efficiency of parallel group trials. Yet, group sequential methods and crossover designs have rarely been paired together. One possible explanation for this could be the absence of a formal proof of how to strongly control the familywise error rate in the case when multiple comparisons will be made. Here, we provide this proof, valid for any number of initial experimental treatments and any number of stages, when results are analyzed using a linear mixed model. We then establish formulae for the expected sample size and expected number of observations of such a trial, given any choice of stopping boundaries. Finally, utilizing the four-treatment, four-period TOMADO trial as an example, we demonstrate that group sequential methods in this setting could have reduced the trials expected number of observations under the global null hypothesis by over 33%.     

Reoptimization framework and policy analysis for maritime inventory routing under uncertainty

We study a maritime inventory routing problem, in which shipments between production and consumption nodes are carried out by a fleet of vessels. The vessels have specific capacities and can be chartered under different agreements. The inventory levels of all consumption nodes and some production nodes should be maintained within specified bounds; for the remaining production nodes, orders should be picked up within pre-defined time windows. We propose a discrete-time mixed-integer programming model. In the face of new information and uncertainty, this optimization model has to be re-solved, as the horizon is rolled forward. We discuss how to account for different sources of uncertainty. We present a rolling-horizon reoptimization framework that allows us to study different policies that impact the quality of the implemented solution, so we can identify the optimal set of policies.     

Privacy-preserving iris authentication using fully homomorphic encryption

Multimedia Tools and Applications - Rapid advancement in technology has led to the use of biometric authentication in every field. In particular, from the past few years, iris recognition systems...     

Calcium phosphates grown on bacterial cellulose template

Bacterial cellulose membranes were employed as templates for calcium phosphates deposition by successive immersion in solutions of Ca(NO₃)₂·4H₂O and (NH₄)₂HPO₄, under ultrasonication. During the wet chemical reaction, mineral phases were loaded on bacterial cellulose fibrils, leading to precursor hybrid composites. These were subjected to a lyophilisation procedure in order to preserve the 3D porous aspect and afterwards to a thermal treatment with the aim of removing the polymeric phase and generating well crystallized structures. Different types of morphologies were achieved by varying the heating rate, as well as the calcination temperature and period. The as-prepared samples and the final ones were investigated from compositional and structural point of view through X-ray diffraction and Fourier-transform infrared spectroscopy and morphologically concerning by scanning electron microscopy. The magnetic properties were also evaluated in order to demonstrate the suitability of the obtained materials for the development of magnetic scaffolds dedicated to hard tissue applications.