arXiv subjects

Hui Ren

Publications and source records attributed to Hui Ren.

47 records · Page 3Linked to original sources

arXiv2020

Deep Learning-based Four-region Lung Segmentation in Chest Radiography for COVID-19 Diagnosis

Purpose. Imaging plays an important role in assessing severity of COVID 19 pneumonia. However, semantic interpretation of chest radiography (CXR) findings does not include quantitative description of radiographic opacities. Most current AI assisted CXR image analysis framework do not quantify for regional variations of disease. To address these, we proposed a four region lung segmentation method to assist accurate quantification of COVID 19 pneumonia. Methods. A segmentation model to separate left and right lung is firstly applied, and then a carina and left hilum detection network is used, which are the clinical landmarks to separate the upper and lower lungs. To improve the segmentation performance of COVID 19 images, ensemble strategy incorporating five models is exploited. Using each region, we evaluated the clinical relevance of the proposed method with the Radiographic Assessment of the Quality of Lung Edema (RALE). Results. The proposed ensemble strategy showed dice score of 0.900, which is significantly higher than conventional methods (0.854 0.889). Mean intensities of segmented four regions indicate positive correlation to the extent and density scores of pulmonary opacities under the RALE framework. Conclusion. A deep learning based model in CXR can accurately segment and quantify regional distribution of pulmonary opacities in patients with COVID 19 pneumonia.

eess.IV↗

arXiv2020

Atomic origin for hydrogenation promoted bulk oxygen vacancies removal in vanadium dioxide

Oxygen vacancies (VO), a common type of point defects in metal oxides materials, play important roles on the physical and chemical properties. To obtain stoichiometric oxide crystal, the pre-existing VO is always removed via careful post-annealing treatment at high temperature in air or oxygen atmosphere. However, the annealing conditions is difficult to control and the removal of VO in bulk phase is restrained due to high energy barrier of VO migration. Here, we selected VO2 crystal film as the model system and developed an alternative annealing treatment aided by controllable hydrogen doping, which can realizes effective removal of VO defects in VO2-δ crystal at lower temperature. This finding is attributed to the hydrogenation accelerated oxygen vacancies recovery in VO2-δ crystal. Theoretical calculations revealed that the H-doping induced electrons are prone to accumulate around the oxygen defects in VO2-δ film, which facilitates the diffusion of VO and thus makes it easier to be removed. The methodology is expected to be applied to other metal oxides for oxygen-related point defects control.

cond-mat.mtrl-sci↗

arXiv2020

Self-supervised Dynamic CT Perfusion Image Denoising with Deep Neural Networks

Dynamic computed tomography perfusion (CTP) imaging is a promising approach for acute ischemic stroke diagnosis and evaluation. Hemodynamic parametric maps of cerebral parenchyma are calculated from repeated CT scans of the first pass of iodinated contrast through the brain. It is necessary to reduce the dose of CTP for routine applications due to the high radiation exposure from the repeated scans, where image denoising is necessary to achieve a reliable diagnosis. In this paper, we proposed a self-supervised deep learning method for CTP denoising, which did not require any high-dose reference images for training. The network was trained by mapping each frame of CTP to an estimation from its adjacent frames. Because the noise in the source and target was independent, this approach could effectively remove the noise. Being free from high-dose training images granted the proposed method easier adaptation to different scanning protocols. The method was validated on both simulation and a public real dataset. The proposed method achieved improved image quality compared to conventional denoising methods. On the real data, the proposed method also had improved spatial resolution and contrast-to-noise ratio compared to supervised learning which was trained on the simulation data

physics.med-ph↗

arXiv2020

Spectral self-adaptive absorber/emitter for harvesting energy from the sun and outer space

The sun (~6000 K) and outer space (~3 K) are the original heat source and sink for human beings on Earth. The energy applications of absorbing solar irradiation and harvesting the coldness of outer space for energy utilization have attracted considerable interest from researchers. However, combining these two functions in a static device for continuous energy harvesting is unachievable due to the intrinsic infrared spectral conflict. In this study, we developed spectral self-adaptive absorber/emitter (SSA/E) for daytime photothermal and nighttime radiative sky cooling modes depending on the phase transition of the vanadium dioxide coated layer. A 24-hour day-night test showed that the fabricated SSA/E has continuous energy harvesting ability and improved overall energy utilization performance, thus showing remarkable potential in future energy applications.

cond-mat.mtrl-sci↗

arXiv2019

Zooming into Face Forensics: A Pixel-level Analysis

The stunning progress in face manipulation methods has made it possible to synthesize realistic fake face images, which poses potential threats to our society. It is urgent to have face forensics techniques to distinguish those tampered images. A large scale dataset "FaceForensics++" has provided enormous training data generated from prominent face manipulation methods to facilitate anti-fake research. However, previous works focus more on casting it as a classification problem by only considering a global prediction. Through investigation to the problem, we find that training a classification network often fails to capture high quality features, which might lead to sub-optimal solutions. In this paper, we zoom in on the problem by conducting a pixel-level analysis, i.e. formulating it as a pixel-level segmentation task. By evaluating multiple architectures on both segmentation and classification tasks, We show the superiority of viewing the problem from a segmentation perspective. Different ablation studies are also performed to investigate what makes an effective and efficient anti-fake model. Strong baselines are also established, which, we hope, could shed some light on the field of face forensics.

cs.CV↗

arXiv2019

Electron-proton Co-doping Induced Metal-insulator Transition in VO2 Film via Surface Self-assembled Ascorbic Acid Molecules

Charge doping is an effective way to induce metal-insulate transition (MIT) in correlated materials for many important utilizations, which is however practically limited by problem of low stability. In this study, we have achieved pronounced phase modulation and stabilized the metallic state of monoclinic vanadium dioxide (VO2) at room temperature, via a novel electron-proton co-doping mechanism driven by surface absorption of self-assembled L-ascorbic acid (AA) molecules. The ionized AA- species in solution donate effective electrons to the adsorbed VO2 surface, which then electrostatically attract surrounding protons to penetrate, and eventually results in stable hydrogen-doped metallic VO2. The variations of phase and electronic structures as well as the electron occupancy of V-3d/O-2p hybrid orbitals were examined by synchrotron characterizations and first-principle theoretical simulations, which explain the formation of stable metallic state. Importantly, the adsorbed molecules protect hydrogen dopants from escaping out of lattice and thereby stabilize the metallic phase for VO2. Such an electron-proton co-doping mechanism driven by suitable molecules absorption would open a new door for engineering properties of correlated oxide materials.

physics.app-ph↗

arXiv2019

2nd Place and 2nd Place Solution to Kaggle Landmark Recognition andRetrieval Competition 2019

We present a retrieval based system for landmark retrieval and recognition challenge.There are five parts in retrieval competition system, including feature extraction and matching to get candidates queue; database augmentation and query extension searching; reranking from recognition results and local feature matching. In recognition challenge including: landmark and non-landmark recognition, multiple recognition results voting and reranking using combination of recognition and retrieval results. All of models trained and predicted by PaddlePaddle framework. Using our method, we achieved 2nd place in the Google Landmark Recognition 2019 and 2nd place in the Google Landmark Retrieval 2019 on kaggle. The source code is available at here.

cs.CV↗

arXiv2019

Gate-Controlled VO2 Phase Transition for High-Performance Smart Window

VO2 material is promising for developing energy-saving "smart window", owing to its thermochromic property induced by metal-insulator transition (MIT). However, its practical application is greatly limited by the relatively high critical transition temperature (~68oC), low luminous transmittance (<60%) and poor solar energy regulation ability (<15%). Here we developed a reversible and non-volatile electric-field control on the MIT of monoclinic VO2 film. With a solid electrolyte layer assisted gating treatment, we modulated the insertion/extraction of hydrogens into/from VO2 lattice at room temperature, causing tri-state phase transitions accompanied with controllable transmission adjustment. The dramatic increase of visible/infrared transmittance during the phase transition from the metallic (lightly H-doping) to insulating (heavily H-doping) phase leads to an increased solar energy regulation ability up to 26.5%, while keep 70.8% visible-luminous transmittance. These results beat all previous records and even exceeded the theoretical limit for traditional VO2 smart window, removing intrinsic disadvantages of VO2 for energy-saving utilizations. Our findings not only demonstrated an electric-field controlled phase modulation strategy, but also open the door for high-performance VO2-based smart window applications.

physics.app-ph↗

arXiv2018

Multi-Estimator Full Left Ventricle Quantification through Ensemble Learning

Cardiovascular disease accounts for 1 in every 4 deaths in United States. Accurate estimation of structural and functional cardiac parameters is crucial for both diagnosis and disease management. In this work, we develop an ensemble learning framework for more accurate and robust left ventricle (LV) quantification. The framework combines two 1st-level modules: direct estimation module and a segmentation module. The direct estimation module utilizes Convolutional Neural Network (CNN) to achieve end-to-end quantification. The CNN is trained by taking 2D cardiac images as input and cardiac parameters as output. The segmentation module utilizes a U-Net architecture for obtaining pixel-wise prediction of the epicardium and endocardium of LV from the background. The binary U-Net output is then analyzed by a separate CNN for estimating the cardiac parameters. We then employ linear regression between the 1st-level predictor and ground truth to learn a 2nd-level predictor that ensembles the results from 1st-level modules for the final estimation. Preliminary results by testing the proposed framework on the LVQuan18 dataset show superior performance of the ensemble learning model over the two base modules.

cs.CV↗

arXiv2017

Hydrogenating VO2 with protons in acid solution

Hydrogenation is an effective way to tune material property1-5. Traditional techniques for doping hydrogen atoms into solid materials are very costly due to the need for noble metal catalysis and high-temperature/pressure annealing treatment or even high energy proton implantation in vacuum condition5-8. Acid solution contains plenty of freely-wandering protons, but it is difficult to act as a proton source for doping, since the protons always cause corrosions by destroying solid lattices before residing into them. Here we achieve a facile way to hydrogenate monoclinic vanadium dioxide (VO2) with protons in acid solution by attaching suitable metal to it. Considering the Schottky contact at the metal/VO2 interface, electrons flow from metal to VO2 due to workfunction difference and simultaneously attract free protons in acid solution to penetrate, forming the hydrogens dopants inside VO2 lattice. This metal-acid treatment constitutes an electron-proton co-doping strategy, which not only protects the VO2 lattice from corrosion, but also causes pronounced insulator-to-metal transitions. In addition, the metal-acid induced hydrogen doping behavior shows a ripple effect, and it can spread contagiously up to wafer-size area (>2 inch) even triggered by a tiny metal particle attachment (~1.0mm). This will stimulate a new way of simple and cost-effective atomic doping technique for some other oxide materials.

cond-mat.mtrl-sci↗

arXiv2017

Consecutive Insulator-Metal-Insulator Phase Transitions of Vanadium Dioxide by Hydrogen Doping

We report modulation of a reversible phase transition in VO2 films by hydrogen doping. A metallic phase and a new insulating phase are successively observed at room temperature as the doping concentration increases. It is suggested that the polarized charges from doped hydrogens play an important role. These charges gradually occupy V3d-O2p hybridized orbitals and consequently modulate the filling of the VO2 crystal conduction band-edge states, which eventually evolve into new valence band-edge states. This demonstrates the exceptional sensitivity of VO2 electronic properties to electron concentration and orbital occupancy, providing key information for the phase transition mechanism.

cond-mat.str-el↗