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Segmentation of shoulder muscle MRI using a new Region and Edge based Deep Auto-Encoder

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arXiv 2021年

作者： Khan, Saddam Hussain Khan, Asifullah Lee, Yeon Soo Hassan, Mehdi Jeong, Woong Kyo Pattern Recognition Lab Department of Computer & Information Sciences Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad45650 Pakistan Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad45650 Pakistan Center for Mathematical Sciences Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad45650 Pakistan Department of Biomedical Engineering College of Medical Science Catholic University of Daegu South Korea Medical Teaching Institute Peshawar Pakistan Department of Computer Science Air University Islamabad Pakistan Department of Orthopaedic Surgery Korea University College of Medicine Seoul Korea Republic of

Automatic segmentation of shoulder muscle MRI is challenging due to the high variation in muscle size, shape, texture, and spatial position of tears. Manual segmentation of tear and muscle portion is hard, time-consuming, and subjective to pathological expertise. This work proposes a new Region and Edge-based Deep Auto-Encoder (RE-DAE) for shoulder muscle MRI segmentation. The proposed RE-DAE harmoniously employs average and max-pooling operation in the encoder and decoder blocks of the Convolutional Neural Network (CNN). Region-based segmentation incorporated in the Deep Auto-Encoder (DAE) encourages the network to extract smooth and homogenous regions. In contrast, edge-based segmentation tries to learn the boundary and anatomical information. These two concepts, systematically combined in a DAE, generate a discriminative and sparse hybrid feature space (exploiting both region homogeneity and boundaries). Moreover, the concept of static attention is exploited in the proposed RE-DAE that helps in effectively learning the tear region. The performances of the proposed MRI segmentation based DAE architectures have been tested using a 3D MRI shoulder muscle dataset using the hold-out cross-validation technique. The MRI data has been collected from the Korea University Anam Hospital, Seoul, South Korea. Experimental comparisons have been conducted by employing innovative custom-made and existing pre-trained CNN architectures both using transfer learning and fine-tuning. Objective evaluation on the muscle datasets using the proposed SA-RE-DAE showed a dice similarity of 85.58% and 87.07%, an accuracy of 81.57% and 95.58% for tear and muscle regions, respectively. The high visual quality and the objective result suggest that the proposed SA-RE-DAE is able to correctly segment tear and muscle regions in shoulder muscle MRI for better clinical decisions. Copyright © 2021, The Authors. All rights reserved.

关键词： Muscle

Quantum Inspired Optimization for Industrial Scale Problems

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arXiv 2023年

作者： Banner, William P. Hadiashar, Shima Bab Mazur, Grzegorz Menke, Tim Ziolkowski, Marcin Kennedy, Ken Romero, Jhonathan Cao, Yudong Grover, Jeffrey A. Oliver, William D. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Zapata Computing Inc. 100 Federal St 20th Floor BostonMA02110 United States Department of Computational Methods in Chemistry Jagiellonian University Gronostajowa 2 Kraków30-387 Poland Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Harvard University CambridgeMA02138 United States BMW Group Information Technology Research Center 2 Research Dr. GreenvilleSC29607 United States

Model-based optimization, in concert with conventional black-box methods, can quickly solve large-scale combinatorial problems. Recently, quantum-inspired modeling schemes based on tensor networks have been developed which have the potential to better identify and represent correlations in datasets. Here, we use a quantum-inspired model-based optimization method TN-GEO to assess the efficacy of these quantum-inspired methods when applied to realistic problems. In this case, the problem of interest is the optimization of a realistic assembly line based on BMW's currently utilized manufacturing schedule. Through a comparison of optimization techniques, we found that quantum-inspired model-based optimization, when combined with conventional black-box methods, can find lower-cost solutions in certain contexts. Copyright © 2023, The Authors. All rights reserved.

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Time optimal control studies on COVID-19 incorporating adverse events of the antiviral drugs

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arXiv 2021年

作者： Chhetri, Bishal Bhagat, Vijay M. Muthusamy, Swapna Ananth, V.S. Vamsi, D.K.K. Sanjeevi, Carani B. Department of Mathematics and Computer Science Sri Sathya Sai Institute of Higher Learning Prasanthi Nilayam Puttaparthi Anantapur District Andhra Pradesh515134 India Central Leprosy Teaching and Research Institute CLTRI Chennai India Sri Sathya Sai Institute of Higher Learning SSSIHL India

In this study, we first develop SIV model by incorporating the intercellular time delay and analyze the stability of the equilibrium points. The model dynamics admits disease-free equilibrium and the infected equilibrium with their stability, based on the value of basic reproduction number R0. We then frame an optimal control problem with antiviral drugs and second-line drugs as control measures and study their roles in reducing the infected cell count and the viral load. The comparative study done in the optimal control problem suggests that when the first line antiviral drugs shows adverse events, considering these drugs in reduced quantity along with the second line drug would be highly effective in reducing the infected cell and viral load in a COVID infected patients. Later, we formulate a time-optimal control problem with the objective to drive the system from any given initial state to the desired infection-free equilibrium state in minimal time. Using Pontryagin's Minimum Principle the optimal control strategy is shown to be of bang-bang type with possibility of switches between two extreme values of the optimal controls. Numerically, it is shown that the desired infection-free state is achieved in less time when the higher values of both the optimal controls are chosen. Copyright © 2021, The Authors. All rights reserved.

关键词： Bang bang control systems

Predictive Scale-Bridging Simulations through Active Learning

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arXiv 2022年

作者： Karra, Satish Mehana, Mohamed Lubbers, Nicholas Chen, Yu Diaw, Abdourahmane Santos, Javier E. Pachalieva, Aleksandra Pavel, Robert S. Haack, Jeffrey R. McKerns, Michael Junghans, Christoph Kang, Qinjun Livescu, Daniel Germann, Timothy C. Viswanathan, Hari S. Computational Earth Science Group Earth and Environmental Sciences Division Los Alamos National Laboratory NM87545 United States Environmental Molecular Sciences Laboratory Pacific Northwest National Laboratory RichlandWA99354 United States Information Sciences Group Computer Computational and Statistical Sciences Division Los Alamos National Laboratory Los AlamosNM87545 United States Department of Mechanics and Aerospace Engineering Southern University of Science and Technology Shenzen518055 China RadiaSoft LLC 6525 Gunpark Dr. Suite 370-411 BoulderCO80301 United States Applied Computer Science Group Computer Computational and Statistical Sciences Division Los Alamos National Laboratory Los AlamosNM87545 United States Computational Physics and Methods Los Alamos National Laboratory Los AlamosNM87545 United States Physics and Chemistry of Materials Group Theoretical Division Los Alamos National Laboratory Los AlamosNM87545 United States

Throughout computational science, there is a growing need to utilize the continual improvements in raw computational horsepower to achieve greater physical fidelity through scale-bridging over brute-force increases in the number of mesh elements. For instance, quantitative predictions of transport in nanoporous media, critical to hydrocarbon extraction from tight shale formations, are impossible without accounting for molecular-level interactions. Similarly, inertial confinement fusion simulations rely on numerical diffusion to simulate molecular effects such as non-local transport and mixing without truly accounting for molecular interactions. With these two disparate applications in mind, we develop a novel capability which uses an active learning approach to optimize the use of local fine-scale simulations for informing coarse-scale hydrodynamics. Our approach addresses three challenges: forecasting continuum coarse-scale trajectory to speculatively execute new fine-scale molecular dynamics calculations, dynamically updating coarse-scale from fine-scale calculations, and quantifying uncertainty in neural network models. © 2022, CC BY.

关键词： Molecular dynamics

Text classification based on Multi-granularity attention Hybrid Neural Network

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arXiv 2020年

作者： Liu, Zhenyu Huang, Haiwei Lu, Chaohong Lyu, Shengfei Tao, Zhenchao Department of Science and Technology Teaching China University of Political Science and Law Beijing100088 China School of Computer and Science Technology University of Science and Technique of China Hefei Anhui230027 China First Affiliated Hospital University of Science and Technique of China Hefei Anhui230027 China

Neural network-based approaches have become the driven forces for Natural Language Processing (NLP) tasks. Conventionally, there are two mainstream neural architectures for NLP tasks: the recurrent neural network (RNN) and the convolution neural network (ConvNet). RNNs are good at modeling long-term dependencies over input texts, but preclude parallel computation. ConvNets do not have memory capability and it has to model sequential data as un-ordered features. Therefore, ConvNets fail to learn sequential dependencies over the input texts, but it is able to carry out high-efficient parallel computation. As each neural architecture, such as RNN and ConvNets, has its own pro and con, integration of different architectures is assumed to be able to enrich the semantic representation of texts, thus enhance the performance of NLP tasks. However, few investigation explores the reconciliation of these seemingly incompatible architectures. To address this issue, we propose a hybrid architecture based on a novel hierarchical multi-granularity attention mechanism, named Multi-granularity Attention-based Hybrid Neural Network (MahNN). The attention mechanism is to assign different weights to different parts of the input sequence to increase the computation efficiency and performance of neural models. In MahNN, two types of attentions are introduced: the syntactical attention and the semantical attention. The syntactical attention computes the importance of the syntactic elements (such as words or sentence) at the lower symbolic level and the semantical attention is used to compute the importance of the embedded space dimension corresponding to the upper latent semantics. We adopt the text classification as an exemplifying way to illustrate the ability of MahNN to understand texts. The experimental results on a variety of datasets demonstrate that MahNN outperforms most of the state-of-the-arts for text classification. Copyright © 2020, The Authors. All rights reserved.

关键词： Recurrent neural networks

Which algorithm to select in sports timetabling?

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arXiv 2023年

作者： Van Bulck, David Goossens, Dries Clarner, Jan-Patrick Dimitsas, Angelos Fonseca, George H.G. Lamas-Fernandez, Carlos Lester, Martin Mariusz Pedersen, Jaap Phillips, Antony E. Rosati, Roberto Maria Faculty of Economics and Business Administration Ghent University Tweekerkenstraat 2 Ghent9000 Belgium Core lab CVAMO FlandersMake@UGent Belgium Applied Algorithmic Intelligence Methods Department Zuse Institute Berlin Takustrase 7 Berlin14195 Germany Department of Informatics University of Ioannina University of Ioannina Greece Computing and Systems Department Federal University of Ouro Preto R. Diogo de Vasconcelos 122 Ouro Preto Brazil Southampton Business School University of Southampton United Kingdom Department of Computer Science University of Reading Reading United Kingdom 7bridges *** 23 Meard St London United Kingdom DPIA University of Udine via delle Scienze 206 Udine33100 Italy

Any sports competition needs a timetable, specifying when and where teams meet each other. The recent International Timetabling Competition (ITC2021) on sports timetabling showed that, although it is possible to develop general algorithms, the performance of each algorithm varies considerably over the problem instances. This paper provides a problem type analysis for sports timetabling, resulting in powerful insights into the strengths and weaknesses of eight state-of-the-art algorithms. Based on machine learning techniques, we propose an algorithm selection system that predicts which algorithm is likely to perform best based on the type of competition and constraints being used (i.e., the problem type) in a given sports timetabling problem instance. Furthermore, we visualize how the problem type relates to algorithm performance, providing insights and possibilities to further enhance several algorithms. Finally, we assess the empirical hardness of the instances. Our results are based on large computational experiments involving about 50 years of CPU time on more than 500 newly generated problem instances. © 2023, CC BY-NC-ND.

关键词： Scheduling algorithms

FOOTPRINT OF PUBLICATION SELECTION BIAS ON META-ANALYSES IN MEDICINE, ENVIRONMENTAL scienceS, PSYCHOLOGY, AND ECONOMICS

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arXiv 2022年

作者： Bartoš, František Maier, Maximilian Wagenmakers, Eric-Jan Nippold, Franziska Doucouliagos, Hristos Ioannidis, John P.A. Otte, Willem M. Sladekova, Martina Deresssa, Teshome K. Bruns, Stephan B. Fanelli, Daniele Stanley, T.D. Department of Psychological Methods University of Amsterdam Netherlands Institute of Computer Science Czech Academy of Sciences Czech Republic Department of Experimental Psychology University College London United Kingdom Department of Economics Deakin University Australia United States Department of Epidemiology and Population Health Stanford University School of Medicine United States Stanford Prevention Research Center Department of Medicine Stanford University School of Medicine United States Department of Biomedical Data Science Stanford University School of Medicine United States Department of Statistics Stanford University School of Humanities and Sciences United States Department of Pediatric Neurology UMC Utrecht Brain Center University Medical Center Utrecht Utrecht University Netherlands School of Psychology University of Sussex United Kingdom Centre for Environmental Sciences Hasselt University Belgium Department of Economics University of Göttingen Germany Department of Methodology London School of Economics and Political Science United Kingdom School of Social Sciences Heriot-Watt University United Kingdom

Publication selection bias undermines the systematic accumulation of evidence. To assess the extent of this problem, we survey over 68,000 meta-analyses containing over 700,000 effect size estimates from medicine (67,386/597,699), environmental sciences (199/12,707), psychology (605/23,563), and economics (327/91,421). Our results indicate that meta-analyses in economics are the most severely contaminated by publication selection bias, closely followed by meta-analyses in environmental sciences and psychology, whereas meta-analyses in medicine are contaminated the least. After adjusting for publication selection bias, the median probability of the presence of an effect decreased from 99.9% to 29.7% in economics, from 98.9% to 55.7% in psychology, from 99.8% to 70.7% in environmental sciences, and from 38.0% to 29.7% in medicine. The median absolute effect sizes (in terms of standardized mean differences) decreased from d = 0.20 to d = 0.07 in economics, from d = 0.37 to d = 0.26 in psychology, from d = 0.62 to d = 0.43 in environmental sciences, and from d = 0.24 to d = 0.13 in medicine. © 2022, CC BY.

关键词： Economic analysis

Anatomy-informed Data Augmentation for Enhanced Prostate Cancer Detection

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arXiv 2023年

作者： Kovacs, Balint Netzer, Nils Baumgartner, Michael Eith, Carolin Bounias, Dimitrios Meinzer, Clara Jäger, Paul F. Zhang, Kevin S. Floca, Ralf Schrader, Adrian Isensee, Fabian Gnirs, Regula Görtz, Magdalena Schütz, Viktoria Stenzinger, Albrecht Hohenfellner, Markus Schlemmer, Heinz-Peter Wolf, Ivo Bonekamp, David Maier-Hein, Klaus H. Heidelberg Germany Division of Radiology DKFZ Heidelberg Germany Medical Faculty Heidelberg Heidelberg University Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany Helmholtz Imaging DKFZ Heidelberg Germany Interactive Machine Learning Group DKFZ Heidelberg Germany Department of Urology University of Heidelberg Medical Center Germany Junior Clinical Cooperation Unit’Multiparametric methods for early detection of prostate cancer’ DKFZ Heidelberg Germany Institute of Pathology University of Heidelberg Medical Center Germany Mannheim University of Applied Sciences Mannheim Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany

Data augmentation (DA) is a key factor in medical image analysis, such as in prostate cancer (PCa) detection on magnetic resonance images. State-of-the-art computer-aided diagnosis systems still rely on simplistic spatial transformations to preserve the pathological label post transformation. However, such augmentations do not substantially increase the organ as well as tumor shape variability in the training set, limiting the model’s ability to generalize to unseen cases with more diverse localized soft-tissue deformations. We propose a new anatomy-informed transformation that leverages information from adjacent organs to simulate typical physiological deformations of the prostate and generates unique lesion shapes without altering their label. Due to its lightweight computational requirements, it can be easily integrated into common DA frameworks. We demonstrate the effectiveness of our augmentation on a dataset of 774 biopsy-confirmed examinations, by evaluating a state-of-the-art method for PCa detection with different augmentation settings. Copyright © 2023, The Authors. All rights reserved.

关键词： Magnetic resonance

Unsupervised embedding learning from uncertainty momentum modeling

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arXiv 2021年

作者： Zhou, Jiahuan Tang, Yansong Su, Bing Wu, Ying Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States Department of Engineering Science University of Oxford OX1 3PJ United Kingdom Beijing Key Laboratory of Big Data Management and Analysis Methods Gaoling School of Artificial Intelligence Renmin University of China Beijing100872 China

Existing popular unsupervised embedding learning methods focus on enhancing the instance-level local discrimination of the given unlabeled images by exploring various negative data. However, the existed sample outliers which exhibit large intra-class divergences or small inter-class variations severely limit their learning performance. We justify that the performance limitation is caused by the gradient vanishing on these sample outliers. Moreover, the shortage of positive data and disregard for global discrimination consideration also pose critical issues for unsupervised learning but are always ignored by existing methods. To handle these issues, we propose a novel solution to explicitly model and directly explore the uncertainty of the given unlabeled learning samples. Instead of learning a deterministic feature point for each sample in the embedding space, we propose to represent a sample by a stochastic Gaussian with the mean vector depicting its space localization and covariance vector representing the sample uncertainty. We leverage such uncertainty modeling as momentum to the learning which is helpful to tackle the outliers. Furthermore, abundant positive candidates can be readily drawn from the learned instance-specific distributions which are further adopted to mitigate the aforementioned issues. Thorough rationale analyses and extensive experiments are presented to verify our superiority. © 2021, CC BY.

关键词： Embeddings