327 research outputs found
Unsupervised Lesion Detection via Image Restoration with a Normative Prior
Unsupervised lesion detection is a challenging problem that requires
accurately estimating normative distributions of healthy anatomy and detecting
lesions as outliers without training examples. Recently, this problem has
received increased attention from the research community following the advances
in unsupervised learning with deep learning. Such advances allow the estimation
of high-dimensional distributions, such as normative distributions, with higher
accuracy than previous methods.The main approach of the recently proposed
methods is to learn a latent-variable model parameterized with networks to
approximate the normative distribution using example images showing healthy
anatomy, perform prior-projection, i.e. reconstruct the image with lesions
using the latent-variable model, and determine lesions based on the differences
between the reconstructed and original images. While being promising, the
prior-projection step often leads to a large number of false positives. In this
work, we approach unsupervised lesion detection as an image restoration problem
and propose a probabilistic model that uses a network-based prior as the
normative distribution and detect lesions pixel-wise using MAP estimation. The
probabilistic model punishes large deviations between restored and original
images, reducing false positives in pixel-wise detections. Experiments with
gliomas and stroke lesions in brain MRI using publicly available datasets show
that the proposed approach outperforms the state-of-the-art unsupervised
methods by a substantial margin, +0.13 (AUC), for both glioma and stroke
detection. Extensive model analysis confirms the effectiveness of MAP-based
image restoration.Comment: Extended version of 'Unsupervised Lesion Detection via Image
Restoration with a Normative Prior' (MIDL2019
Unsupervised Detection of Lesions in Brain MRI using constrained adversarial auto-encoders
Lesion detection in brain Magnetic Resonance Images (MRI) remains a
challenging task. State-of-the-art approaches are mostly based on supervised
learning making use of large annotated datasets. Human beings, on the other
hand, even non-experts, can detect most abnormal lesions after seeing a handful
of healthy brain images. Replicating this capability of using prior information
on the appearance of healthy brain structure to detect lesions can help
computers achieve human level abnormality detection, specifically reducing the
need for numerous labeled examples and bettering generalization of previously
unseen lesions. To this end, we study detection of lesion regions in an
unsupervised manner by learning data distribution of brain MRI of healthy
subjects using auto-encoder based methods. We hypothesize that one of the main
limitations of the current models is the lack of consistency in latent
representation. We propose a simple yet effective constraint that helps mapping
of an image bearing lesion close to its corresponding healthy image in the
latent space. We use the Human Connectome Project dataset to learn distribution
of healthy-appearing brain MRI and report improved detection, in terms of AUC,
of the lesions in the BRATS challenge dataset.Comment: 9 pages, 5 figures, accepted at MIDL 201
Crustal Anisotropy from the Birefringence of P-to-S Converted Waves: Bias Associated with P-Wave Anisotropy
Many researchers have used the birefringence of P‑to‑S converted waves from the Moho discontinuity to constrain the anisotropy of Earth’s crust. However, this practice ignores the substantial influence that anisotropy has on the initial amplitude of the converted wave, which adds to the splitting acquired during its propagation from Moho to the seismometer. We find that large variations in Ps birefringence estimates with back-azimuth occur theoretically in the presence of P‑wave anisotropy, which normally accompanies S‑wave anisotropy. The variations are largest for crustal anisotropy with a tilted axis of symmetry, a geometry that is often neglected in birefringence interpretations, but is commonly found in Earth’s crust. We simulated globally-distributed P‑coda datasets for 36 distinct 4‑layer crustal models with combinations of elliptical shear anisotropy or compressional anisotropy, and also incorporated the higher-order anisotropic Backus parameter C. We tested both horizontal and tilted symmetry-axis geometries and tested the birefringence tradeoff associated with Ps converted phases at the top and bottom of a thin high‑ or low‑velocity basal layer. We computed composite receiver functions (RFs) with harmonic regression over back azimuth, using multipletaper correlation with moveout corrections for the epicentral distances of 471 events, to simulate a realistic data set. We estimate Ps birefringence from the radial and transverse RFs, a strategy that is similar to previous studies. We find that Ps splitting can be a useful indicator of bulk crustal anisotropy only under restricted circumstance, either in media with no compressional anisotropy, or if the symmetry axis is horizontal throughout. In other, more-realistic cases, the inferred fast polarization of Ps birefringence estimated from synthetic RFs tends either to drift with back-azimuth, form weak penalty-function minima, or return splitting times that depend on the thickness of an anisotropic layer, rather than the birefringence accumulated within it.
Probing triple-Higgs productions via decay channel at a 100 TeV hadron collider
The quartic self-coupling of the Standard Model Higgs boson can only be
measured by observing the triple-Higgs production process, but it is
challenging for the Large Hadron Collider (LHC) Run 2 or International Linear
Collider (ILC) at a few TeV because of its extremely small production rate. In
this paper, we present a detailed Monte Carlo simulation study of the
triple-Higgs production through gluon fusion at a 100 TeV hadron collider and
explore the feasibility of observing this production mode. We focus on the
decay channel , investigating
detector effects and optimizing the kinematic cuts to discriminate the signal
from the backgrounds. Our study shows that, in order to observe the Standard
Model triple-Higgs signal, the integrated luminosity of a 100 TeV hadron
collider should be greater than ab. We also explore the
dependence of the cross section upon the trilinear () and quartic
() self-couplings of the Higgs. We find that, through a search in
the triple-Higgs production, the parameters and can be
restricted to the ranges and , respectively. We also
examine how new physics can change the production rate of triple-Higgs events.
For example, in the singlet extension of the Standard Model, we find that the
triple-Higgs production rate can be increased by a factor of .Comment: 33 pages, 11 figures, added references, corrected typos, improved
text, affiliation is changed. This is the publication versio
Frequency steps and compositions determine properties of needling sensation during electroacupuncture
AbstractObjectiveTo investigate the relationship of electro-parameters and the electroacupuncture sensation (EAS), which is thought to be an important factor for optimal treatment.MethodsThe frequency steps and compositions of three frequently used electrical stimulations were set when the switch of the electroacupuncture apparatus was turned to the second or third grade of the dense-disperse frequency wave (DD2 and DD3, respectively) or the second grade of the continuous wave (C2). Three groups of patients according to the three electroacupuncture stimulations were divided again into three sub-groups according to the stimulated acupoints: the face acupoint Quanliao (SI 18), the upper-limb acupoint Quchi (LI 11) and the back acupoint Dachangshu (BL 25). The EAS values were measured every 5 min during 30 min electroacupuncture treatments using a visual analogue scale.ResultsThe frequency compositions of the three electroacupuncture stimulations were 3.3 and 33 Hz, 12.5 and 66.7 Hz, and 3.3 and 3.3 Hz; each frequency step was 30, 54 and 0 Hz, respectively. In each sub-group of the C2 group, the EAS values from 10 to 30 min were significantly weaker than at 0 min. The sensation fluctuations in the DD2 and DD3 groups were different during the 30 min.ConclusionThe greater the frequency step of the electroacupuncture stimulation, the longer the needling sensation lasted. The electroacupuncture stimulations of the DD3 group were unsuitable for the facial acupoint because of its painful and uncomfortable EAS, but more suitable for the back acupoint
Mesh-MLP: An all-MLP Architecture for Mesh Classification and Semantic Segmentation
With the rapid development of geometric deep learning techniques, many
mesh-based convolutional operators have been proposed to bridge irregular mesh
structures and popular backbone networks. In this paper, we show that while
convolutions are helpful, a simple architecture based exclusively on
multi-layer perceptrons (MLPs) is competent enough to deal with mesh
classification and semantic segmentation. Our new network architecture, named
Mesh-MLP, takes mesh vertices equipped with the heat kernel signature (HKS) and
dihedral angles as the input, replaces the convolution module of a ResNet with
Multi-layer Perceptron (MLP), and utilizes layer normalization (LN) to perform
the normalization of the layers. The all-MLP architecture operates in an
end-to-end fashion and does not include a pooling module. Extensive
experimental results on the mesh classification/segmentation tasks validate the
effectiveness of the all-MLP architecture.Comment: 8 pages, 6 figure
Analytical computation of settlement displacement of buried pipeline caused by excavation
Excavation can induce the settlement deformation of adjacent buried pipeline. Obtaining the deformation is of great significance for evaluating the normal use and safety of pipeline. In this study, based on the elastic foundation beam theory, the calculation formula of the settlement displacement of buried pipeline is derived, and the influence of the two parameters of the surface center settlement and the calculation length on the settlement displacement of pipelines is emphatically analyzed. According to the geometric relationship between the buried pipeline and the edge of the foundation pit, the calculation length can be used to divide into five working conditions. The rationality of the analytical method is verified by comparing with two cases. In the case of excavation in Beijing, the spatio-temporal variation rules of settlement displacement of buried pipeline were analyzed. The results show that the variated trend of the calculated settlement displacement in different periods is consistent with that from measurement, but the calculated value is slightly higher than the measured value. The surface center settlement has a significant influence on the pipeline settlement displacement. larger input causes more concave settlement curve, and smaller input leads to smoother settlement curve. The decreasing rate of settlement displacement increases from the central point to end, and decreases near the boundary point, with the range of about one tenth of the calculated length. The proposed method in this study is conservative in the evaluation of pipeline settlement, which is a supplement to the existing settlement calculation theory of buried pipelines, and can provide a important information for predicting the distribution of pipeline settlement in the early stage of construction
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