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 4b2γ4b2\gamma 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 $HHH\rightarrow b\bar{b}b\bar{b}\gamma\gamma$, 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 $1.8\times 10^4$ ab$^{-1}$. We also explore the dependence of the cross section upon the trilinear ($\lambda_3$) and quartic ($\lambda_4$) self-couplings of the Higgs. We find that, through a search in the triple-Higgs production, the parameters $\lambda_3$ and $\lambda_4$ can be restricted to the ranges $[-1, 5]$ and $[-20, 30]$, 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 $\mathcal{O}(10)$.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

Platelet activation: a promoter for psoriasis and its comorbidity, cardiovascular disease

Psoriasis is a chronic inflammatory skin disease with a prevalence of 0.14% to 1.99%. The underlying pathology is mainly driven by the abnormal immune responses including activation of Th1, Th17, Th22 cells and secretion of cytokines. Patients with psoriasis are more likely to develop cardiovascular disease (CVD) which has been well recognized as a comorbidity of psoriasis. As mediators of hemostasis and thromboinflammation, platelets play an important part in CVD. However, less is known about their pathophysiological contribution to psoriasis and psoriasis-associated CVD. A comprehensive understanding of the role of platelet activation in psoriasis might pave the path for more accurate prediction of cardiovascular (CV) risk and provide new strategies for psoriasis management, which alleviates the increased CV burden associated with psoriasis. Here we review the available evidence about the biomarkers and mechanisms of platelet activation in psoriasis and the role of platelet activation in intriguing the common comorbidity, CVD. We further discussed the implications and efficacy of antiplatelet therapies in the treatment of psoriasis and prevention of psoriasis-associated CVD