16,126 research outputs found
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 , and we
conclude that the based six-parameter CDM model is preferred.
Exploring some extensions to the CDM models, we find that the equation
of state of dark energy reads in the CDM model, the
effective relativistic degrees of freedom in the Universe is
in the CDM model and
the spatial curvature parameter is in the
CDM model at confidence level (C.L.), and the
C.L. upper bounds on the sum of three active neutrinos masses are eV for the normal hierarchy (NH) and eV for the
inverted hierarchy (IH) with .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
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