Unsupervised Body Part Regression via Spatially Self-ordering Convolutional Neural Networks

Automatic body part recognition for CT slices can benefit various medical image applications. Recent deep learning methods demonstrate promising performance, with the requirement of large amounts of labeled images for training. The intrinsic structural or superior-inferior slice ordering information in CT volumes is not fully exploited. In this paper, we propose a convolutional neural network (CNN) based Unsupervised Body part Regression (UBR) algorithm to address this problem. A novel unsupervised learning method and two inter-sample CNN loss functions are presented. Distinct from previous work, UBR builds a coordinate system for the human body and outputs a continuous score for each axial slice, representing the normalized position of the body part in the slice. The training process of UBR resembles a self-organization process: slice scores are learned from inter-slice relationships. The training samples are unlabeled CT volumes that are abundant, thus no extra annotation effort is needed. UBR is simple, fast, and accurate. Quantitative and qualitative experiments validate its effectiveness. In addition, we show two applications of UBR in network initialization and anomaly detection.Comment: Oral presentation in ISBI1

New cosmological constraints with extended-Baryon Oscillation Spectroscopic Survey DR14 quasar sample

We update the constraints on the cosmological parameters by adopting the Planck data released in 2015 and Baryon Acoustic Oscillation (BAO) measurements including the new DR14 quasar sample measurement at redshift $z=1.52$, and we conclude that the based six-parameter $\Lambda$CDM model is preferred. Exploring some extensions to the $\Lambda$CDM models, we find that the equation of state of dark energy reads $w=-1.036\pm 0.056$ in the $w$CDM model, the effective relativistic degrees of freedom in the Universe is $N_\text{eff}=3.09_{-0.20}^{+0.18}$ in the $N_\text{eff}+\Lambda$CDM model and the spatial curvature parameter is $\Omega_k=(1.8\pm1.9)\times 10^{-3}$ in the $\Omega_k+\Lambda$CDM model at $68\%$ confidence level (C.L.), and the $95\%$ C.L. upper bounds on the sum of three active neutrinos masses are $\sum m_\nu<0.16$ eV for the normal hierarchy (NH) and $\sum m_\nu<0.19$ eV for the inverted hierarchy (IH) with $\Delta\chi^2\equiv \chi^2_\text{NH}-\chi^2_\text{IH}=-1.25$.Comment: 9 pages, 5 figures, 4 table

Design of Dual-band Branch-Line Coupler Based on Shunt Open-Circuit DCRLH Cells

In this article, the shunt open-circuit dual composite right/left-handed (DCRLH) cell is initially proposed and one dual-band branch-line coupler based on the proposed cells is designed. It is found that, compared with DCRLH cell, the frequency selectivity, matching condition and adjustment range of the shunt open-circuit DCRLH cell improve greatly. Moreover, the shunt open-circuit DCRLH cell exhibits two adjustable frequency points with -90degrees phase shift within its first two passbands. In order to explore this exotic property effectively, the influence of the primary geometrical parameter is investigated through parametric analysis. Thus, one dual-band branch-line coupler based on the shunt open-circuit DCRLH cells is designed. Both simulated and measured results indicate that comparative performance is achieved. Different from part of previous dual-band branch line couplers, for the proposed coupler, the signs of phase difference of two output ports within the two operating frequency bands are identical with each other. This branch-line coupler is quite suitable for the application which is sensitive to the variation of phase difference and its effective area is compact