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A Coarse-to-Fine Ensembling Framework for Head and Neck Tumor and Lymph Segmentation in CT and PET Images 1

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3rd 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2022, held in Conjunction with the 25th international conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022

作者： Sun, Xiao An, Chengyang Wang, Lisheng Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9783031274206

ISBN: (纸本)9783031274190

Head and neck (H &N) cancer is one of the most prevalent cancers [1]. In its treatment and prognosis analysis, tumors and metastatic lymph nodes may play an important role but their manual segmentations are time-consuming and laborious. In this paper, we propose a coarse-to-fine ensembling framework to segment the H &N tumor and metastatic lymph nodes automatically from Positron Emission Tomography (PET) and Computed Tomography (CT) images. the framework consists of three steps. the first step is to locate the head region in CT images. the second step is a coarse segmentation, to locate the tumor and lymph region of interest (ROI) from the head region. the last step is a fine segmentation, to get the final precise predictions of tumors and metastatic lymph nodes, where we proposed a ensembling refinement model. this framework is evaluated quantitatively with aggregated Dice Similarity Coefficient (DSC) of 0.77782 in the task 1 of the HECKTOR 2022 challenge[2, 3] as team SJTU426. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Tumors

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Data Selection for Statistical Machine Translation

Data Selection for Statistical Machine Translation

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the 6th international conference on Natural Language Processing and Knowledge Engineering(第六届IEEE自然语言处理与知识工程国际会议 NLP-KE 2010)

作者： Peng LIU Yu ZHOU Chengqing ZONG National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781424468973

the bilingual language corpus has a great effect on the performance of a statistical machine translation system. More data will lead to better performance. However, more data also increase the computational load. In this paper, we propose methods to estimate the sentence weight and select more informative sentences from the training corpus and the development corpus based on the sentence weight. the translation system is built and tuned on the compact corpus. the experimental results show that we can obtain a competitive performance with much less data.

关键词： Data selection corpus optimization statistical machine translation

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A case study on the analysis of behavior patterns and pattern changes in smart environments

A case study on the analysis of behavior patterns and patter...

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6th international Work-conference on Ambient Assisted Living, IWAAL 2014

作者： Lago, Paula Jiménez-Guarín, Claudia Roncancio, Claudia Systems and Computing Engineering Department School of Engineering Universidad de los Andes Colombia Univ. Grenoble Alpes LIG France

ISBN: (纸本)9783319131047

Societies need to devise mechanisms of caring for the well aging of the increasing number of seniors, as it is very important for elderly people to maintain their independence. Smart environments are being devised as a form of care in what has been called ambient assisted living. A smart environment should be able to respond in case of emergency or risk and inform any abnormal behavior. Still, not much research is done to understand behavior patterns, temporal changes and other particularities that can affect the effectiveness of smart environments in ambient assisted living. We explored the behavior of two adults in a smart environment in order to reveal temporal, spatial and sequential relations among the activities as well as the changes that these relations undergo overtime and across individuals. this paper presents an analysis of three human behavior patterns: temporal, location and frequency. these patterns are mined on two experimental subjects using the dataset provided by the CASAS project. Our analysis evidences how temporal, spatial and sequential patterns differ from person to person, day to day and after some time. Learning personalized behaviors and identifying and adapting to changes is a crucial aspect for smart environments since one-fit-all solutions are not suitable. © Springer international Publishing Switzerland 2014.

关键词： Assisted living

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Efficient significant point generation technique for shape descriptor

Efficient significant point generation technique for shape d...

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international conference on Information, Communications and Signal Processing

作者： M. R. Rahman M. A. Ali M. S. Islam G. Sorwar Department of Computer Science & Engineering Daffodil International University Dhaka Bangladesh School of Commerce and Management Information Technology Unit Southern Cross University Coffs Harbour NSW Australia

ISBN: (纸本)9781424409822;1424409829

the existing significant point generation technique such as convex hull is unable to find out the proper significant points for irregular shaped objects. To address this issue, an algorithm namely efficient significant point generation (ESG) has been proposed in this paper that is able to find out proper significant points which will further be used to produce required shape descriptor using parametric curve generation techniques. Experimental results confirm the superior performance of ESG algorithm over convex hull in approximating significant points for all types of irregular shaped objects.

关键词： Polynomials Image processing pattern recognition Active shape model Computer science Business Information management Technology management Engineering management Information technology

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Comparative Analysis of the Accuracy of Backpropagation and Learning Vector Quantisation for pattern recognition of Hijaiyah Letters

Comparative Analysis of the Accuracy of Backpropagation and ...

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international conference on Information and Communication Technology for the Muslim World (ICT4M)

作者： Nimas Ayu Mailani Victor Amrizal Nashrul Hakiem UIN Syarif Hidayatullah Jakarta Indonesia

ISBN: (纸本)9781509045228

the Artificial Neural Network (ANN) is a branch of science in the field of artificial intelligence and is created from adapting the workings of the human brain. Backpropagation (BP) and Learning Vector Quantisation (LVQ) are two of many methods used to recognise patterns. Both are supervised training methods with different approaches. BP uses an error value to recognise patterns or images, while LVQ uses distance values as an indicator classification of patterns in class. this study conducted a simulation of pattern recognition to compare the accuracy of BP and LVQ in terms of recognising Hijaiyah letter patterns (Arabic characters) to see how the number of epochs and the value of learning rate during the training process affects the accuracy. Simulations carried out 28 targets or Hijaiyah letter with parameter epoch 25, 100, 500, 1000, 3000 and 5000 as well as the learning rate of 0.001, 0.05, 0.01, 0.1, 0.25 and 0.05. Overall, the resulting accuracy on Backpropagation method is better compared to LVQ. It is influenced by the value of learning rate and the number of epochs. Accuracy of the Backpropagation training is less influenced by the value of learning rate, but it becomes more accurate if the number of epochs used for training a lot in accordance with the number of samples used. While LVQ, contrary to Backpropagation, the accuracy of the results of training are affected by the value of learning rate and is less affected by the number of epoch.

关键词： Backpropagation Vector quantization Training Artificial neural networks Training data Character recognition

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A novel gender classification method based on MapReduce

A novel gender classification method based on MapReduce

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IEEE international conference on Software Engineering and Service Sciences (ICSESS)

作者： Tong Cui Haifeng Zhao Science and Technology on Information Systems Engineering Laboratory Nanjing China

ISBN: (纸本)9781479983544

A novel parallelize gender recognition method with MapReduce is presented, which successfully comprise several machine leaning algorithms which are employed for gender recognition. the mass of face sample images are gathered and separated as train dataset and test dataset, and Local Binary pattern (LBP) features are extracted when those sample sets are pre-processed and made ready for following operations. And Principle Component Analysis (PCA) is applied to train dataset to extract the most distinguishing features. three classification algorithms: Support Vector Machine(SVM), k-Nearest Neighborhood (k-NN) and Adaboost are implemented and compared to determine the most suitable and successful algorithm for gender parallelize machine learning (GPML). To achieve the shortest execution time, we propose to apply GPML with MapReduce to avoid parallelizing above three algorithms while also improving their scalability to big datasets. the results show that this method reduces the training computational complexity significantly when the number of computing nodes increases while gaining better speedup rates and extending performance than those on parallelize Adaboost.

关键词： face recognition feature extraction image classification learning (artificial intelligence) parallel processing principal component analysis support vector machines Adaboost GPML LBP features MapReduce PCA SVM classification algorithm face sample images gender classification method gender parallelize machine learning k-nearest neighborhood local binary pattern feature extraction machine leaning algorithms parallelized gender recognition method principle component analysis support vector machine test dataset train dataset training computational complexity gender recognition parallel meta-learning Support Vector Network principal components analysis Sorting algorithm Image classification PARALLEL PROCESSING (COMPUTERS) Facial recognition Passive Cutaneous Anaphylaxis Feature extraction Dataset Gender ADABOOST execution time Classification Methods

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Gene Expression pattern recognition Algorithm Based on Deep Learning

Gene Expression Pattern Recognition Algorithm Based on Deep ...

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IEEE international conference on Artificial Intelligence and Computer Applications (ICAICA)

作者： Xinzhuo Tu Yuanyuan Li Department of Pathology Air Force Medical Center PLA Beijing China China Medical University Shenyang China Nanchong Central Hospital Nanchong China

ISBN: (数字)9798350377255

ISBN: (纸本)9798350377262

With the rapid development of bioinformatics, the analysis of gene expression data has become crucial for understanding life processes, disease mechanisms, and drug development. For binary classification tasks, the performance of most traditional machine learning models is highly dependent on the quality of features, which often requires researchers to have a deep professional background, and artificially constructed feature vectors may lose the original information in protein sequences. this study aims to use deep learning models to extract high-order features from gene expression data, in order to solve the problem of gene expression pattern recognition. Firstly, by analyzing the limitations of traditional biological methods in gene recognition, a new approach combining recurrent neural networks (RNNs) and Transformer models is proposed. RNN can effectively capture temporal dependencies in sequence data, while Transformer, through its multi head self-attention mechanism, can more effectively process long sequence data and capture complex dependencies in gene expression sequences. through experiments on multiple publicly available gene expression datasets, it has been demonstrated that the Transformer model outperforms the RNN model, demonstrating its superiority in feature extraction. then, in response to the problem of low efficiency in processing long sequences with the Transformer model, this study further proposes the CNN Transformer model. the model first uses one-dimensional convolutional layers to extract local features from gene expression data, and then uses Transformer layers to capture long-range dependencies between these features. the experimental results show that the CNN Transformer model exhibits higher balanced accuracy on multiple independent test sets, demonstrating the effectiveness of the model structure. Finally, this article discusses the potential application value of this method in fields such as personalized medicine and early disease dia

关键词： Deep learning Proteins Recurrent neural networks Biological system modeling Transformers Feature extraction Data models Gene expression Convolutional neural networks bioinformatics

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Test System for Defect Detection in Construction Materials with Ultrasonic Waves by Support Vector Machine and Neural Network

Test System for Defect Detection in Construction Materials w...

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international conference on Soft Computing and Intelligent Systems

作者： Saowanee Saechai Waree Kongprawechnon Raktipong Sahamitmongkol School of Information Computer and communication Technology Sirindhorn International Institute of Technology Thammasat University Construction and Maintenance Technology Research Center Sirindhorn International Institute of Technology Thammasat University

ISBN: (纸本)9781467327428

this paper introduces a newly developed test system for structure inspection and evaluation of cement-based products by applying ultrasonic test with support vector machine (SVM) classifier. In other words, this paper represents a novel method based on SVM for defect detection, classification of number of defects, and identification of defect materials. With the system, pattern of ultrasonic waves for each case of specimen can be obtained from direct and indirect measurements. Machine learning algorithm called support vector machine and artificial neural network (ANN) are employed for classification and verification of the wave patterns obtained from different samples. By applying the system, the presence or absence of a defect in mortar can be identified. Moreover, the system can also classify the number of defects and identify the defect materials being inside the mortar. For classification, input features are extracted in different ways and the numbers of training sets are varied. Base on the results from SVM, the signals extracted in frequency domain gives better performance than time domain. Using a larger training set can give more satisfactory results. In this article, the methodology is explained and the classification results are discussed. the effectiveness of the developed test system is evaluated. Comparison of the classification results that obtained by between SVM and ANN classifiers is also demonstrated. this study shows that this technique based on pattern recognition has a high potential for practical inspection of concrete structure.

关键词： Ultrasonic pulse velocity Defect detection pattern recognition Support vector machine Neural network

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Equalized orthogonal map-based DNA microarray data analysis for cancer diagnosis

Equalized orthogonal map-based DNA microarray data analysis ...

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international conference on Information, Communications and Signal Processing

作者： Jagdish C. Patra Sineepong Detpongsante Pramod K. Meher Ee Luang Ang School of Computer Engineering Nanyang Technological University Singapore

ISBN: (纸本)9781424409822;1424409829

A new approach to cancer diagnosis involves the analysis of gene expression data. However, to make sense of multidimensional DNA microarray data is a challenging task. Beyond 3 dimensions, it is very difficult for humans to make intuitive interpretation of how each data point is distributed, how they are interrelated, and how they should be categorized. Equalized orthogonal map (EOM), an approach to dimension reduction was able to produce topologically correct mappings for visualization and classification of cancer types. Performance comparison with a self-organizing map (SOM) is also discussed. Experimental results show that EOM performs well with DNA microarray data classification.

关键词： Cancer DNA Data analysis Data visualization Principal component analysis Topology Medical treatment pattern recognition Gene expression Multidimensional systems

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Lace Fabric Image Retrieval Using Siamese Neural Network

Lace Fabric Image Retrieval Using Siamese Neural Network

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IEEE international conference on Signal and Image Processing (ICSIP)

作者： DongDong Xu Yueyang Li HaiChi Luo Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi China College of Internet of Things Engineering Jiangnan University Wuxi China

ISBN: (纸本)9781665446006

An efficient lace fabric image retrieval method based on DCNN learning features is proposed in this paper. Fine-tuning with Siamese Neural Network is used to learn effective feature of lace fabric image. During the process of training the Siamese Neural Network, hard negative pairs are selective to achieve fast convergence and good performance. the DCNN learning features are combined with the unique shape feature to enable accurate and efficient retrieval of massive image data. Experimental results demonstrate the effectiveness of retrieval performance of the proposed algorithm and possible practical application of the retrieval system in lace fabric industry to improve management efficiency.

关键词： Training Industries Shape Image processing conferences Neural networks Image retrieval

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