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High-Quality Interpolation of Breast DCE-MRI Using Learned Transformations 1

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5th international workshop on Simulation and synthesis in Medical Imaging (SASHIMI)

作者： Wang, Hongyu Feng, Jun Pan, Xiaoying Yang, Di Chen, Baoying Xian Univ Posts & Telecommun Sch Comp Sci & Technol Xian 710121 Shaanxi Peoples R China Xian Univ Posts & Telecommun Shaanxi Key Lab Network Data Anal & Intelligent P Xian 710121 Shaanxi Peoples R China Northwest Univ Dept Informat Sci & Technol Xian 7101127 Shaanxi Peoples R China Fourth Mil Med Univ Tangdu Hosp Dept Radiol Funct & Mol Imaging Key Lab Shaanxi Prov Xian 710038 Shaanxi Peoples R China Xian Int Med Ctr Hosp Imaging Diag & Treatment Ctr Xian 710110 Shaanxi Peoples R China

ISBN: (数字)9783030595203

ISBN: (纸本)9783030595197;9783030595203

Dynamic Contrast Enhancement Magnetic Resonance Imaging (DCE-MRI) is gaining popularity for computer aided diagnosis (CAD) of breast cancer. However, the performance of these CAD systems is severely affected when the number of DCE-MRI series is inadequate, inconsistent or limited. This work presents a High-Quality DCE-MRI Interpolation method based on Deep Neural Network (HQI-DNN) using an end-to-end trainable Convolutional Neural Network (CNN). It gives a good solution to the problem of inconsistent and inadequate quantity of DCE-MRI series for breast cancer analysis. Starting from a nested CNN for feature learning, the dynamic contrast enhanced features of breast lesions are learned by bidirectional contrast transformations between DCE-MRI series. Each transformation contains the spatial deformation field and the intensity change, enabling a variable-length multiple series interpolation of DCE-MRI. We justified the proposed method through extensive experiments on our dataset. It produced a more efficient result of breast DCE-MRI interpolation than other methods.

关键词： DCE-MRI Interpolation CNN Breast cancer

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Determination of pattern Recognition Problems based on a Pythagorean Fuzzy Correlation Measure from Statistical Viewpoint

Determination of Pattern Recognition Problems based on a Pyt...

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international workshop on Advanced Computational Intelligence (IWACI)

作者： Paul Augustine Ejegwa Yuming Feng Shiping Wen Wei Zhang Key Laboratory of Intelligent Information Processing and Control Chongqing Three Gorges University Wanzhou Chongqing China School of Computer Science and Engineering Chongqing Three Gorges University Wanzhou Chongqing China Australian AI Institute University of Technology Sydney Ultimo Australia School of Three Gorges Artificial Intelligence Chongqing Three Gorges University Wanzhou Chongqing China

By considering all the three parameters describing Pythagorean fuzzy set, we introduce a new technique of computing correlation coefficient between Pythagorean fuzzy sets from statistical perspective. The correlation coefficient value obtain via this technique shows strength of correlation between the Pythagorean fuzzy sets and indicates whether the Pythagorean fuzzy sets under consideration are related negatively or positively in contrast to other existing correlation coefficient approaches in Pythagorean fuzzy context, which only assess the strength of relationship. Certain numerical examples are considered to ascertain the authenticity of this method over similar techniques studied in intuitionistic/Pythagorean fuzzy contexts. Some pattern recognition problems are resolved with the aid of the new technique. For higher productivity sake, this technique could be approached from an object-oriented perspective.

关键词： Correlation coefficient Productivity Fuzzy sets Correlation Uncertainty pattern recognition Performance analysis

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A cooperative trust evaluation scheme for tactical wireless sensor networks 20

A cooperative trust evaluation scheme for tactical wireless ...

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3rd international Conference on Software Engineering and Information Management, ICSIM 2020 - and its workshop 2020 the 3rd international Conference on Big Data and Smart Computing, ICBDSC 2020

作者： Lim, Jihun Keum, Dooho Ko, Young-Bae Department of Computer Engineering Ajou University Suwon Korea Republic of

ISBN: (纸本)9781450376907

In this paper, we address an important issue of trust evaluation for tactical wireless sensor networks, where energy-hungry sensor nodes are deployed in harsh and hostile areas for the purpose of surveillance and reconnaissance. In particular, due to the limited resources of the node, it is of great importance to detect and isolate malicious nodes in an energy-efficient manner. Most of previous work adopt some proactive scanning method, requiring periodical exchange of control messages even in ordinary times and thus significant computational overhead for updating trust values per all nodes over the network. In order to provide more energy efficient evaluation method (while yet preventing any intelligent attacks such as selective forwarding and false positive problem attacks), we propose a novel cooperative trust evaluation scheme in which the initial trust level of nodes measured by ondemand trust rating approach is re-evaluated later on by a root or gateway node based on a pattern analysis. Through a preliminary simulation study using OPNET simulator, we compare our scheme to the existing work and prove that it can achieve the higher detection rate with much less energy consumption. © 2020 Association for Computing Machinery.

关键词： Sensor nodes

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Evaluation of Unconditioned Deep Generative synthesis of Retinal Images 20th

Evaluation of Unconditioned Deep Generative Synthesis of Ret...

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20th international Conference on Advanced Concepts for intelligent Vision Systems, ACIVS 2020

作者： Kaplan, Sinan Lensu, Lasse Laaksonen, Lauri Uusitalo, Hannu Computer Vision and Pattern Recognition Laboratory Lappeenranta-Lahti University of Technology LUT P.O. Box 20 Lappeenranta53850 Finland Department of Ophthalmology Faculty of Health and Biotechnology Tampere University and Tays Eye Center Tampere Finland

ISBN: (纸本)9783030406042

Retinal images have been increasingly important in clinical diagnostics of several eye and systemic diseases. To help the medical doctors in this work, automatic and semi-automatic diagnosis methods can be used to increase the efficiency of diagnostic and follow-up processes, as well as enable wider disease screening programs. However, the training of advanced machine learning methods for improved retinal image analysis typically requires large and representative retinal image data sets. Even when large data sets of retinal images are available, the occurrence of different medical conditions is unbalanced in them. Hence, there is a need to enrich the existing data sets by data augmentation and introducing noise that is essential to build robust and reliable machine learning models. One way to overcome these shortcomings relies on generative models for synthesizing images. To study the limits of retinal image synthesis, this paper focuses on the deep generative models including a generative adversarial network and a variational autoencoder to synthesize images from noise without conditioning on any information regarding to the retina. The models are trained with the Kaggle EyePACS retinal image set, and for quantifying the image quality in a no-reference manner, the generated images are compared with the retinal images of the DiaRetDB1 database using common similarity metrics. © 2020, Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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Diagnostic signal nonstationarity reduction to predict the helicopter transmission state on the basis of intelligent information technologies 2

Diagnostic signal nonstationarity reduction to predict the h...

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2nd international workshop on computer Modeling and intelligent Systems, CMIS 2019

作者： Lymariev, Ihor Subbotin, Sergey Oliinyk, Andrii Drokin, Ihor Zaporizhzhia National Technical University Zhukovsky str. 64 Zaporizhzhia69063 Ukraine 'Motor Sich' JSC Motorostroiteley avenue 15 Zaporizhzhia69068 Ukraine

analysis process and transformation of non-stationary diagnostic signals to reduce the degree of their non-stationarity and the subsequent synthesis of neural network predictive model based on them are considered. The software which allows to apply the developed methods for processing of signals of diagnostics of transmission of the helicopter and on their basis to build the forecasting model for the solution of practical problems of technical diagnostics is developed. © 2019 CEUR-WS. All rights reserved.

关键词： Neural networks

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Grain Segmentation in Atomic Force Microscopy for Thin-Film Deposition Quality Control 2nd

Grain Segmentation in Atomic Force Microscopy for Thin-Film ...

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2nd international workshop on Recent Advances in Digital Security: Biometrics and Forensics, BioFor 2019, 1st international workshop on pattern Recognition for Cultural Heritage, PatReCH 2019, 1st international workshop eHealth in the Big Data and Deep Learning Era, e-BADLE 2019, international workshop on Deep Understanding Shopper Behaviors and Interactions in intelligent Retail Environments, DEEPRETAIL 2019 and Industrial session held at the 20th international Conference on Image analysis and Processing, ICIAP 2019

作者： Lanza, Nicolò Romeo, Alessandro Cristani, Marco Setti, Francesco Department of Computer Science University of Verona Verona Italy

ISBN: (纸本)9783030307530

In this paper we propose an image segmentation method specifically designed to detect crystalline grains in microscopic images. We build on the watershed segmentation approach;we propose a preprocessing pipeline to generate a topographic map exploiting the physical nature of the incoming data (i.e. Atomic Force Microscopy) to emphasize grain boundaries and generate seeds for basins. Experimental results show the effectiveness of the proposed method against grain segmentation implementations available in commercial software on a new labelled dataset with an average improvement of over 20% in precision and recall over the standard implementation of watershed segmentation. © 2019, Springer Nature Switzerland AG.

关键词： Atomic force microscopy

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analysis based on Distributed Representations of Various Parts Images in Four-scene Comics Story Dataset 13

Analysis based on Distributed Representations of Various Par...

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13th IAPR international workshop on Graphics Recognition (GREC) / 15th international Conference on Document analysis and Recognition (ICDAR)

作者： Terauchi, Akira Mori, Naoki Ueno, Miki Osaka Prefecture Univ Dept Comp Sci & Intelligent Syst Osaka Japan Osaka Inst Technol Dept Elect & Informat Syst Engn Osaka Japan

ISBN: (纸本)9781728150543

Ways of understanding human creations with artificial intelligence have increased;however, those are still known as one of the most difficult tasks. Our challenge is to find ways to understand four-scene comics by AI. To achieve this aim, we used the novel dataset called "Four-scene Comics Story Dataset", which is the first dataset made by researchers and comic artists to develop AI creations. We focused on illustration touches of comics which are determined by comic artists. First, we applied convolutional autoencoder (CAE) models to this dataset to get distributed representations, then applied classifiers in order to classify the different touches. The prediction offers an indirect measure of the distributed representations. We already investigated the experiment using original scenes. To show the advantageous of the dataset, the proposed method is confirmed by computer simulations taking data of various pattern of removing image parts such as "eyes" in scene images of the four-scene comics story dataset structure as examples.

关键词： Comic Engineering Convolutional Autoencoder Four-scene Comics Story Dataset Deep Learning

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2nd international workshop on Recent Advances in Digital Security: Biometrics and Forensics, BioFor 2019, 1st international workshop on pattern Recognition for Cultural Heritage, PatReCH 2019, 1st international workshop eHealth in the Big Data and Deep Learning Era, e-BADLE 2019, international workshop on Deep Understanding Shopper Behaviors and Interactions in intelligent Retail Environments, DEEPRETAIL 2019 and Industrial session held at the 20th international Conference on Image analysis and Processing, ICIAP 2019

2nd International Workshop on Recent Advances in Digital Sec...

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ISBN: (纸本)9783030307530

The proceedings contain 39 papers. The special focus in this conference is on Recent Advances in Digital Security. The topics include: Modern vs Diplomatic Transcripts for Historical Handwritten Text Recognition;improving Ancient Cham Glyph Recognition from Cham Inscription Images Using Data Augmentation and Transfer Learning;Oracle Bone Inscription Detector Based on SSD;shot Boundary Detection for Automatic Video analysis of Historical Films;the Epistle to Cangrande Through the Lens of Computational Authorship Verification;a Cockpit of Measures for Image Quality Assessment in Digital Film Restoration;augmented Reality for the Valorization and Communication of Ruined Architecture;classification of Arabic Poems: from the 5th to the 15th Century;a Page-Based Reject Option for Writer Identification in Medieval Books;on the Cross-Finger Similarity of Vein patterns;minimizing Training Data for Reliable Writer Identification in Medieval Manuscripts;fusion of Visual and Anamnestic Data for the Classification of Skin Lesions with Deep Learning;slide Screening of Metastases in Lymph Nodes via Conditional, Fully Convolutional Segmentation;A Learning Approach for Informative-Frame Selection in US Rheumatology Images;a Serious Game to Support Decision Making in Medical Education;nerve Contour Tracking for Ultrasound-Guided Regional Anesthesia;Skin Lesions Classification: A Radiomics Approach with Deep CNN;semantic 3D Object Maps for Everyday Robotic Retail Inspection;collecting Retail Data Using a Deep Learning Identification Experience;a Large Scale Trajectory Dataset for Shopper Behaviour Understanding;Improving Multi-scale Face Recognition Using VGGFace2;An IOT Edge-Fog-Cloud Architecture for Vision Based Pallet Integrity;the Vending Shopper Science Lab: Deep Learning for Consumer Research.

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6D Pose Estimation for Industrial Applications 2nd

6D Pose Estimation for Industrial Applications

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作者： Cunico, Federico Carletti, Marco Cristani, Marco Masci, Fabio Conigliaro, Davide Department of Computer Science University of Verona Verona Italy Genova Italy The Edge Company Srl Rimini Italy Humatics Srl Verona Italy

ISBN: (纸本)9783030307530

Object pose estimation is important for systems and robots to interact with the environment where the main challenge of this task is the complexity of the scene caused by occlusions and clutters. A key challenge is performing pose estimation leveraging on both RGB and depth information: prior works either extract information from the RGB image and depth separately or use costly post-processing steps, limiting their performances in highly cluttered scenes and real-time applications. Traditionally, the pose estimation problem is tackled by matching feature points between 3D models and images. However, these methods require rich textured models. In recent years, the raising of deep learning has offered an increasing number of methods based on neural networks, such as DSAC++, PoseCNN, DenseFusion and SingleShotPose. In this work, we present a comparison between two recent algorithms, DSAC++ and DenseFusion, focusing on computational cost, performance and applicability in the industry. © 2019, Springer Nature Switzerland AG.

关键词： Textures

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Oracle Bone Inscription Detector Based on SSD 2nd

Oracle Bone Inscription Detector Based on SSD

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作者： Meng, Lin Lyu, Bing Zhang, Zhiyu Aravinda, C.V. Kamitoku, Naoto Yamazaki, Katsuhiro Department of Electronic and Computer Engineering Ritsumeikan University KusatsuShiga525-8577 Japan Department of Computer Science and Engineering NMAM Institute of Technology Nitte Karkala TalukKarnataka India

ISBN: (纸本)9783030307530

This paper introduces Oracle Bone Inscription Detector which based on Single Shot Multibox Detector, for segmenting and recognizing Oracle Bone Inscriptions from rubbing images. Oracle Bone Inscription which is the one of the oldest and most mysterious ancient characters, used about 3000 years ago in china, and lots of these literature are stored by rubbing images. Because that only few of specialists understand the Oracle Bone Inscriptions, lots of Oracle Bone Inscriptions are waiting for be understood for helping researchers know the history, culture, economy etc. Currently, deep learning method of single shot multibox detector achieves a good performance for segmentation and recognition, and may achieve a good performance for Oracle Bone Inscription detection. However, we fond the Single Shot Multibox Detector is weak at small object detection. This research equips and extends Single Shot Multibox Detector for Oracle Bone Inscription detection, and analyzes the mis-detection for achieving a better accuracy. The experimental results shows that Precision, Recall and F value achieve 0.95, 0.83 and 0.88 respectively, and proves the effectiveness of extended Single Shot Multibox Detector in Oracle Bone Inscription detection. © 2019, Springer Nature Switzerland AG.

关键词： Object detection

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