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An effective POS-CNN-based adaptive model for classifying brain tumour from MrI images

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AIP Conference Proceedings 2023年第1期2914卷

作者： Sajeev ram Arumugam L. Thanga Mariappan K. Makanyadevi r. Balakrishna Sankar Ganesh Karuppasamy Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India Department of Smart Computing School of Information Technology and Engineering Vellore Institute of Technology vellore India Department of Computer science and Engineering M. Kumarasamy College of Engineering Karur India Department of Artificial Intelligence and Data Science Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai India Department of Computer science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

One of the crucial reasons for death in people is a brain tumour. It will likely develop into malignancy if not appropriately and promptly treated. As a result, early brain tumour diagnosis is a crucial requirement. The Convolutional Neural Network and Particle Swarm Optimization are combined to construct a proposed framework for Brain Tumour detection from MrI Images. PSO is used to increase segmentation precision and to train the proposed system using CNN. Along with PSO, Scale Invariant Feature Transform (SIFT) is employed as a feature descriptor to improve the system’s classification accuracy. Brain tumour MrI images from the BraTS 2018 dataset were used as input sets for this research. A diagnosis accuracy of 98.76 % was obtained from the results. The proposed technique has better accuracy when the assessment features of the proposed system are evaluated with a few existing approaches.

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An OpenMetBuoy dataset of Marginal Ice Zone dynamics collected around Svalbard in 2022 and 2023

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arXiv 2024年

作者： rabault, Jean Taelman, Catherine Idžanović, Martina Hope, Gaute Nose, Takehiko Kristoffersen, Yngve Jensen, Atle Breivik, Øyvind Bryhni, Helge Thomas Hoppmann, Mario demchev, denis Korosov, Anton Johansson, Malin Eltoft, Torbjørn dagestad, Knut-Frode röhrs, Johannes Eriksson, Leif Moro, Marina durán rikardsen, Edel S.U. Waseda, Takuji Kodaira, Tsubasa Lohse, Johannes desjonquères, Thibault Olsen, Sveinung Gundersen, Olav de Aguiar, Victor Cesar Martins Karlsen, Truls Babanin, Alexander Voermans, Joey Park, Jeong-Won Müller, Malte Norwegian Meteorological Institute IT Department Oslo0313 Norway UiT The Arctic University of Norway Dept. of Physics and Technology Tromsø9037 Norway Norwegian Meteorological Institute Division for Ocean and Ice Oslo0371 Norway Norwegian Meteorological Institute Division for Oceanography and Marine Meteorology Bergen5007 Norway The University of Tokyo Graduate School of Frontier Sciences Kashiwa277-8561 Japan University of Bergen Department of Earth Science Bergen5007 Norway University of Oslo Department of Mathematics Oslo0313 Norway University of Bergen Geophysical Institute Bergen5007 Norway Alfred-Wegener-Institut Helmholtz-Zentrum fuer Polar- und Meeresforschung Bremerhaven27580 Germany Department of Space Earth and Environment Chalmers University of Technology Gothenburg41296 Sweden Ocean and Sea Ice Remote Sensing Group Nansen Environmental and Remote Sensing Center Bergen5006 Norway Norwegian Meteorological Institute Remote sensing and data management division Oslo Norway University of Oslo Department of Geosciences Oslo0313 Norway Japan Agency for Marine-Earth Science and Technology Yokosuka237-0061 Japan Gothenburg University Gothenburg40508 Sweden Department of Infrastructure Engineering University of Melbourne Melbourne Australia Center of Remote Sensing and GIS Korea Polar Research Institute Incheon21990 Korea Republic of Norwegian Meteorological institute R&D Department Oslo0313 Norway

Sea ice is a key element of the global Earth system, with a major impact on global climate and regional weather. Unfortunately, accurate sea ice modeling is challenging due to the diversity and complexity of underlying physics happening there, and a relative lack of ground truth observations. This is especially true for the Marginal Ice Zone (MIZ), which is the area where sea ice is affected by incoming ocean waves. Waves contribute to making the area dynamic, and due to the low survival time of the buoys deployed there, the MIZ is challenging to monitor. In 2022-2023, we released 79 OpenMetBuoys (OMBs) around Svalbard, both in the MIZ and the ocean immediately outside of it. OMBs are affordable enough to be deployed in large number, and gather information about drift (GPS position) and waves (1-dimensional elevation spectrum). This provides data focusing on the area around Svalbard with unprecedented spatial and temporal resolution. We expect that this will allow to perform validation and calibration of ice models and remote sensing algorithms. © 2024, CC BY.

关键词： Water waves

Towards robust Waveform-Based Acoustic Models

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arXiv 2021年

作者： Oglic, dino Cvetkovic, Zoran Sollich, Peter renals, Steve Yu, Bin The Applied Analytics and Ai Data Sciences and Ai BioPharmaceuticals R&d AstraZeneca CambridgeCB2 8PA United Kingdom The Department of Engineering King's College London LondonWC2R 2LS United Kingdom The Institute for Theoretical Physics University of Göttingen Göttingen37073 Germany The Center for Speech Technology Research University of Edinburgh EdinburghEH8 9AB United Kingdom The Departments of Statistics and Electrical Engineering and Computer Sciences Uc Berkeley BerkeleyCA94720 United States

We study the problem of learning robust acoustic models in adverse environments, characterized by a significant mismatch between training and test conditions. This problem is of paramount importance for the deployment of speech recognition systems that need to perform well in unseen environments. First, we characterize data augmentation theoretically as an instance of vicinal risk minimization, which aims at improving risk estimates during training by replacing the delta functions that define the empirical density over the input space with an approximation of the marginal population density in the vicinity of the training samples. More specifically, we assume that local neighborhoods centered at training samples can be approximated using a mixture of Gaussians, and demonstrate theoretically that this can incorporate robust inductive bias into the learning process. We then specify the individual mixture components implicitly via data augmentation schemes, designed to address common sources of spurious correlations in acoustic models. To avoid potential confounding effects on robustness due to information loss, which has been associated with standard feature extraction techniques (e.g., FBANK and MFCC features), we focus on the waveform-based setting. Our empirical results show that the approach can generalize to unseen noise conditions, with 150% relative improvement in out-of-distribution generalization compared to training using the standard risk minimization principle. Moreover, the results demonstrate competitive performance relative to models learned using a training sample designed to match the acoustic conditions characteristic of test utterances. Copyright © 2021, The Authors. All rights reserved.

关键词： delta functions

Benchmarking the variational quantum eigensolver through simulation of the ground state energy of prebiotic molecules on high-performance computers

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arXiv 2020年

作者： Lolur, P. rahm, M. Skogh, M. García-Álvarez, L. Wendin, Goeran Department of Chemistry and Chemical Engineering Chalmers University of Technology GothenburgSE-412 96 Sweden Data Science & Modelling Pharmaceutical Science R&D AstraZeneca Gothenburg Sweden Applied Quantum Physics Laboratory Department of Microtechnology and Nanoscience-MC2 Chalmers University of Technology GothenburgSE-412 96 Sweden Quantum Technology Laboratory Department of Microtechnology and Nanoscience-MC2 Chalmers University of Technology GothenburgSE-412 96 Sweden

We use the Variational Quantum Eigensolver (VQE) as implemented in the Qiskit software package to compute the ground state energy of small molecules derived from water, H2O, and hydrogen cyanide, HCN. The work aims to benchmark algorithms for calculating the electronic structure and energy surfaces of molecules of relevance to prebiotic chemistry, beginning with water and hydrogen cyanide, and to run them on the available simulated and physical quantum hardware. The numerical calculations of the algorithms for small quantum processors allow us to design more efficient protocols to be run in real hardware, as well as to analyze their performance. Future implementations on accessible quantum processing prototypes will benchmark quantum computers and provide tests of quantum advantage with heuristic quantum algorithms. Copyright © 2020, The Authors. All rights reserved.

关键词： Molecules

Causal discovery with Cascade Nonlinear Additive Noise Models

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arXiv 2019年

作者： Cai, ruichu Qiao, Jie Zhang, Kun Zhang, Zhenjie Hao, Zhifeng School of Computers Guangdong University of Technology China Department of philosophy Carnegie Mellon University Singapore R&D Yitu Technology Ltd School of Mathematics and Big Data Foshan University China

Identification of causal direction between a causal-effect pair from observed data has recently attracted much attention. Various methods based on functional causal models have been proposed to solve this problem, by assuming the causal process satisfies some (structural) constraints and showing that the reverse direction violates such constraints. The nonlinear additive noise model has been demonstrated to be effective for this purpose, but the model class is not transitive–even if each direct causal relation follows this model, indirect causal influences, which result from omitted intermediate causal variables and are frequently encountered in practice, do not necessarily follow the model constraints;as a consequence, the nonlinear additive noise model may fail to correctly discover causal direction. In this work, we propose a cascade nonlinear additive noise model to represent such causal influences–each direct causal relation follows the nonlinear additive noise model but we observe only the initial cause and final effect. We further propose a method to estimate the model, including the unmeasured intermediate variables, from data, under the variational auto-encoder framework. Our theoretical results show that with our model, causal direction is identifiable under suitable technical conditions on the data generation process. Simulation results illustrate the power of the proposed method in identifying indirect causal relations across various settings, and experimental results on real data suggest that the proposed model and method greatly extend the applicability of causal discovery based on functional causal models in nonlinear cases. Copyright © 2019, The Authors. All rights reserved.

关键词： Additives

One-dimensional deep low-rank and sparse network for accelerated MrI

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arXiv 2021年

作者： Wang, Zi Qian, Chen Guo, di Sun, Hongwei Li, rushuai Zhao, Bo Qu, Xiaobo Department of Electronic Science Biomedical Intelligent Cloud R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Xiamen University China School of Computer and Information Engineering Xiamen University of Technology Xiamen China United Imaging Research Institute of Intelligent Imaging Beijing China Department of Nuclear Medicine Nanjing First Hospital Nanjing Medical University Nanjing China Department of Biomedical Engineering Oden Institute for Computational Engineering and Sciences University of Texas at Austin Austin United States

deep learning has shown astonishing performance in accelerated magnetic resonance imaging (MrI). Most state-of-the-art deep learning reconstructions adopt the powerful convolutional neural network and perform 2d convolution since many magnetic resonance images or their corresponding k-space are in 2d. In this work, we present a new approach that explores the 1d convolution, making the deep network much easier to be trained and generalized. We further integrate the 1d convolution into the proposed deep network, named as One-dimensional deep Low-rank and Sparse network (OdLS), which unrolls the iteration procedure of a low-rank and sparse reconstruction model. Extensive results on in vivo knee and brain datasets demonstrate that, the proposed OdLS is very suitable for the case of limited training subjects and provides improved reconstruction performance than state-of-the-art methods both visually and quantitatively. Additionally, OdLS also shows nice robustness to different undersampling scenarios and some mismatches between the training and test data. In summary, our work demonstrates that the 1d deep learning scheme is memory-efficient and robust in fast MrI. Copyright © 2021, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

rainfall Monitoring Using Microwave Links from Cellular Communication Networks: The dutch Experience

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Rainfall Monitoring Using Microwave Links from Cellular Comm...

IEEE/SP Workshop on Statistical Signal Processing (SSP)

作者： Aart Overeem Hidde Leijnse remko Uijlenhoet Hydrology and Quantitative Water Management Group Wageningen University & Research R&D Observations and Data Technology Royal Netherlands Meteorological Institute (KNMI)

Microwave links from commercial cellular communication networks have been used for rainfall monitoring in The Netherlands since 2003. Here we report on the start of our work on this topic using a dedicated microwave link in 1999, our first trails with commercial microwave links (CMLs) from Vodafone in 2003, our first published results in 2007, our work on sources of error and uncertainties in rainfall retrievals using microwave links of different lengths and frequencies, and finally the fruitful collaboration with T-Mobile NL, which lead to areal rainfall estimation for the rotterdam metropolitan area in 2009 and 2010 and country-wide rainfall mapping for the entire land surface area of The Netherlands since 2012. Our current work on this topic follows three lines of research: (1) further refinement of our rainfall retrieval and mapping algorithm; (2) further quantification of sources of error and uncertainties using a dedicated experimental setup of multiple microwave links and a line configuration of disdrometers for ground validation; (3) promotion of the international replication of this method for rainfall monitoring through collaboration with partners in Europe and beyond.

关键词： Microwave communication Microwave measurement rain Attenuation Monitoring Communication networks

Fabrication of amino acid conjugated polymeric micelles for controlled anticancer drug delivery using radiation and pH-stimuli-triggering systems

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Journal of drug delivery Science and technology 2023年 80卷

作者： Addisu, Kefyalew dagnew Lee, Wen-Yi Tsai, Hsieh-Chih Wu, Szu-Yuan Graduate Institute of Applied Science and Technology National Taiwan University of Science and Technology Taipei 106 Taiwan Faculty of Chemical and Food Engineering Bahir Dar University P. O. Box 26 Bahir Dar Ethiopia Advance Membrane Materials Center National Taiwan University of Science and Technology Taipei 106 Taiwan R&D Center for Membrane Technology Chung Yuan Christian University Chungli Taoyuan 320 Taiwan Department of Food Nutrition and Health Biotechnology College of Medical and Health Science Asia University Taichung 413 Taiwan Big Data Center Lo-Hsu Medical Foundation Lotung Poh-Ai Hospital Yilan 256 Taiwan Division of Radiation Oncology Department of Medicine Lo-Hsu Medical Foundation Lotung Poh-Ai Hospital Yilan 256 Taiwan Department of Healthcare Administration College of Medical and Health Science Asia University Taichung 413 Taiwan Cancer Center Lo-Hsu Medical Foundation Lotung Poh-Ai Hospital Yilan 256 Taiwan Graduate Institute of Business Administration Fu Jen Catholic University Taipei 242 Taiwan Centers for Regional Anesthesia and Pain Medicine Taipei Municipal Wan Fang Hospital Taipei Medical University Taipei 110 Taiwan

drug delivery to the tumor site and the minimization of adverse effects on surrounding tissues remain major issues in pharmaceutical research. In this study, pH- and radiation-responsive PLGA-PEG-PLGA amphiphilic copolymers were terminally conjugated with tryptophan (Try), tyrosine (Tyr), or histidine (His), utilizing PEG as an initiator and Sn (Oct) 2 as a catalyst. Micelles having an inner core of hydrophobic blocks (PLGA and Try/Tyr/His) and a corona of hydrophilic PEG were developed to improve stability. The structural destabilization of the micelles caused by radiation and pH was characterized by an increase in dOX fluorescence intensities and a shift in particle size. Both Try-PLGA-PEG-PLGA-Try and Tyr-PLGA-PEG-PLGA-Tyr micelles displayed low cytotoxicity in MTT experiments with NIH-3T3 and HeLa cell lines at 100 μg/mL concentrations, with cell viabilities of more than 95.33% and 92.33%, respectively. Samples containing 10 μg/mL dOX, on the other hand, demonstrated significant anticancer efficacy against HeLa cells (28.54% and 29.97% of the cells remained viable after treatment with Tyr and Try, respectively). The cellular uptake experiment also revealed that dOX-loaded micelles were up taken by the cell and that irradiation and pH-sensitive stimuli induced drug release in the cytoplasm and nucleus of the cancer cell. Finally, these findings imply that the synthesized copolymers could be used as stimuli-responsive carriers for anti-cancer drug delivery. © 2023

关键词： dual-responsive PLGA-PEG-PLGA radiation-sensitive Tryptophan Tyrosine

One for Multiple: Physics-informed Synthetic data Boosts Generalizable deep Learning for Fast MrI reconstruction

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arXiv 2023年

作者： Wang, Zi Yu, Xiaotong Wang, Chengyan Chen, Weibo Wang, Jiazheng Chu, Ying-Hua Sun, Hongwei Li, rushuai Li, Peiyong Yang, Fan Han, Haiwei Kang, Taishan Lin, Jianzhong Yang, Chen Chang, Shufu Shi, Zhang Hua, Sha Li, Yan Hu, Juan Zhu, Liuhong Zhou, Jianjun Lin, Meijing Guo, Jiefeng Cai, Congbo Chen, Zhong Guo, di Yang, Guang Qu, Xiaobo Department of Electronic Science Intelligent Medical Imaging R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Xiamen University China Human Phenome Institute Fudan University China Philips Healthcare China Siemens Healthineers Ltd. China United Imaging Research Institute of Intelligent Imaging China Department of Nuclear Medicine Nanjing First Hospital China Shandong Aoxin Medical Technology Company China Department of Radiology The First Affiliated Hospital of Xiamen University China Department of Radiology Zhongshan Hospital Affiliated to Xiamen University China China Department of Cardiology Shanghai Institute of Cardiovascular Diseases Zhongshan Hospital Fudan University China Department of Radiology Zhongshan Hospital Fudan University China Department of Cardiovascular Medicine Heart Failure Center Ruijin Hospital Lu Wan Branch Shanghai Jiaotong University School of Medicine China Department of Radiology Ruijin Hospital Shanghai Jiaotong University School of Medicine China Medical Imaging Department The First Affiliated Hospital of Kunming Medical University China Xiamen Key Laboratory of Clinical Transformation of Imaging Big Data and Artificial Intelligence China Department of Applied Marine Physics and Engineering Xiamen University China Department of Microelectronics and Integrated Circuit Xiamen University China School of Computer and Information Engineering Xiamen University of Technology China Department of Bioengineering Imperial College London United Kingdom

Magnetic resonance imaging (MrI) is a widely used radiological modality renowned for its radiation-free, comprehensive insights into the human body, facilitating medical diagnoses. However, the drawback of prolonged scan times hinders its accessibility. The k-space undersampling offers a solution, yet the resultant artifacts necessitate meticulous removal during image reconstruction. Although deep Learning (dL) has proven effective for fast MrI image reconstruction, its broader applicability across various imaging scenarios has been constrained. Challenges include the high cost and privacy restrictions associated with acquiring large-scale, diverse training data, coupled with the inherent difficulty of addressing mismatches between training and target data in existing dL methodologies. Here, we present a novel Physics-Informed Synthetic data learning framework for Fast MrI, called PISF. PISF marks a breakthrough by enabling generalized dL for multi-scenario MrI reconstruction through a single trained model. Our approach separates the reconstruction of a 2d image into many 1d basic problems, commencing with 1d data synthesis to facilitate generalization. We demonstrate that training dL models on synthetic data, coupled with enhanced learning techniques, yields in vivo MrI reconstructions comparable to or surpassing those of models trained on matched realistic datasets, reducing the reliance on real-world MrI data by up to 96%. Additionally, PISF exhibits remarkable generalizability across multiple vendors and imaging centers. Its adaptability to diverse patient populations has been validated through evaluations by ten experienced medical professionals. PISF presents a feasible and cost-effective way to significantly boost the widespread adoption of dL in various fast MrI applications. Importantly, it alleviates the ethical and practical challenges associated with in vivo human data acquisitions, opening new horizons for advancing fast MrI technologies. Copyright © 20

关键词： Magnetic resonance imaging