This study presents a deep learning model for efficient intracranial hemorrhage(ICH)detection and subtype classification on non-contrast head computed tomography(CT)*** refers to bleeding in the skull,leading to the m...
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This study presents a deep learning model for efficient intracranial hemorrhage(ICH)detection and subtype classification on non-contrast head computed tomography(CT)*** refers to bleeding in the skull,leading to the most critical life-threatening health condition requiring rapid and accurate *** is classified as intra-axial hemorrhage(intraventricular,intraparenchymal)and extra-axial hemorrhage(subdural,epidural,subarachnoid)based on the bleeding location inside the *** computer-aided diagnoses(CAD)-based schemes have been proposed for ICH detection and classification at both slice and scan ***,these approaches performonly binary classification and suffer from a large number of parameters,which increase storage ***,the accuracy of brain hemorrhage detection in existing models is significantly low for medically critical *** overcome these problems,a fast and efficient system for the automatic detection of ICH is *** designed a double-branch model based on xception architecture that extracts spatial and instant features,concatenates them,and creates the 3D spatial context(common feature vectors)fed to a decision tree classifier for final *** data employed for the experimentation was gathered during the 2019 Radiologist Society of North America(RSNA)brain hemorrhage detection *** model outperformed benchmark models and achieved better accuracy in intraventricular(99.49%),subarachnoid(99.49%),intraparenchymal(99.10%),and subdural(98.09%)categories,thereby justifying the performance of the proposed double-branch xception architecture for ICH detection and classification.
Low-light image enhancement is an important issue in digital image processing area. However, in practice, it is still a difficult problem, especially how to balance computational cost and restore result. To tackle thi...
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Modeling regular expressions (regexes) has been applied in abundant scenes, but at present, there is a lack of comprehensive modeling for extended operators, which limits their usage in related scenes. To address the ...
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Partial differential equations (PDEs) are an essential computational kernel in physics and engineering. With the advance of deep learning, physics-informed neural networks (PINNs), as a mesh-free method, have shown gr...
Partial differential equations (PDEs) are an essential computational kernel in physics and engineering. With the advance of deep learning, physics-informed neural networks (PINNs), as a mesh-free method, have shown great potential for fast PDE solving in various applications. To address the issue of low accuracy and convergence problems of existing PINNs, we propose a self-training physics-informed neural network, ST-PINN. Specifically, ST-PINN introduces a pseudo label based self-learning algorithm during training. It employs governing equation as the pseudo-labeled evaluation index and selects the highest confidence examples from the sample points to attach the pseudo labels. To our best knowledge, we are the first to incorporate a self-training mechanism into physics-informed learning. We conduct experiments on five PDE problems in different fields and scenarios. The results demonstrate that the proposed method allows the network to learn more physical information and benefit convergence. The ST-PINN outperforms existing physics-informed neural network methods and improves the accuracy by a factor of 1.33x-2.54x.
Non-coding RNAs (ncRNAs), which do not encode proteins, have been implicated in chemotherapy resistance in cancer treatment. Given the high costs and time requirements of traditional biological experiments, there is a...
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Video super-resolution (SR) reconstruction technology aims at obtaining high quality reconstruction of high-resolution (HR) video sequences by inferring the lost detailed information from their low-resolution (LR) cou...
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作者：
Chen, KePeking University
Key Laboratory of High Confidence Software Technologies of Ministry of Education School of Computer Science Beijing100871 China
Persistent inward currents (PICs) play important roles in regulating neural excitability. Results from our previous studies showed that serotonergic (5-HT) neurons of the brainstem expressed PICs. However, little is k...
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Edge computing is a rapidly developing research area known for its ability to reduce latency and improve energy efficiency, and it also has a potential for green computing. Many geographically distributed edge servers...
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JMCT is a large-scale,high-fidelity,three-dimensional general neutron–photon–electron–proton transport Monte Carlo software *** was developed based on the combinatorial geometry parallel infrastructure JCOGIN and t...
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JMCT is a large-scale,high-fidelity,three-dimensional general neutron–photon–electron–proton transport Monte Carlo software *** was developed based on the combinatorial geometry parallel infrastructure JCOGIN and the adaptive structured mesh infrastructure *** is equipped with CAD modeling and visualizes the image *** supports the geometry of the body and the structured/unstructured *** has most functions,variance reduction techniques,and tallies of the traditional Monte Carlo particle transport *** energy models,multi-group and continuous,are *** recent years,some new functions and algorithms have been developed,such as Doppler broadening on-thefly(OTF),uniform tally density(UTD),consistent adjoint driven importance sampling(CADIS),fast criticality search of boron concentration(FCSBC)domain decomposition(DD),adaptive control rod moving(ACRM),and random geometry(RG)*** JMCT is also coupled with the discrete ordinate SNcode JSNT to generate source-biasing factors and weight-window *** present,the number of geometric bodies,materials,tallies,depletion zones,and parallel processors are sufficiently large to simulate extremely complicated device *** can be used to simulate reactor physics,criticality safety analysis,radiation shielding,detector response,nuclear well logging,and dosimetry calculations *** particular,JMCT can be coupled with depletion and thermal-hydraulics for the simulation of reactor nuclear-hot feedback *** paper describes the progress in advanced modeling,high-performance numerical simulation of particle transport,multiphysics coupled calculations,and large-scale parallel computing.
Task offloading is an important concept for edge computing and the Internet of Things(IoT)because computationintensive tasksmust beoffloaded tomore resource-powerful remote *** has several advantages,including increas...
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Task offloading is an important concept for edge computing and the Internet of Things(IoT)because computationintensive tasksmust beoffloaded tomore resource-powerful remote *** has several advantages,including increased battery life,lower latency,and better application performance.A task offloading method determines whether sections of the full application should be run locally or offloaded for execution *** offloading choice problem is influenced by several factors,including application properties,network conditions,hardware features,and mobility,influencing the offloading system’s operational *** study provides a thorough examination of current task offloading and resource allocation in edge computing,covering offloading strategies,algorithms,and factors that influence *** offloading and partial offloading strategies are the two types of offloading *** algorithms for task offloading and resource allocation are then categorized into two parts:machine learning algorithms and non-machine learning *** examine and elaborate on algorithms like Supervised Learning,Unsupervised Learning,and Reinforcement Learning(RL)under machine *** the non-machine learning algorithm,we elaborate on algorithms like non(convex)optimization,Lyapunov optimization,Game theory,Heuristic Algorithm,Dynamic Voltage Scaling,Gibbs Sampling,and Generalized Benders Decomposition(GBD).Finally,we highlight and discuss some research challenges and issues in edge computing.
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