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Improving Ultrasound Tongue Contour Extraction Using U-Net and Shape Consistency-Based Regularizer

Improving Ultrasound Tongue Contour Extraction Using U-Net a...

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IEEE International conference on Acoustics, Speech and Signal processing

作者： Ming Feng Yin Wang Kele Xu Huaimin Wang Bo Ding Tongji University Shanghai China National Key Lab of Parallel and Distributed Processing Changsha China College of Computer National University of Defense Technology Changsha China

B-mode ultrasound tongue imaging is widely used to visualize the tongue motion, due to its appearing properties. Extracting the tongue surface contour in the B-mode ultrasound image is still a challenge, while it is a prerequisite for further quantitative analysis. Recently, deep learning-based approach has been adopted in this task. However, the standard deep models fail to address faint contour when the ultrasound wave goes parallel to the tongue surface. To address the faint or missing contours in the sequence, we explore the shape consistency-based regularizer, which can take sequential information into account. By incorporating the regularizer, the deep model not only can extract frame-specific contours, but also can enforce the similarity between the contours extracted from adjacent frames. Extensive experiments are conducted both on the synthetic and real ultrasound tongue imaging dataset and the results demonstrate the effectiveness of proposed method. To better promote the research in this field, we have released our code at.

关键词： image segmentation Tongue Ultrasonic imaging Surface waves Shape Statistical analysis Imaging

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distributed Computing method for Synthetic Aperture Radar Compressed Sensing Imaging based on MapReduce 3

Distributed Computing method for Synthetic Aperture Radar Co...

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3rd IEEE International conference on Electronic Information and Communication Technology, ICEICT 2020

作者： Zheng, Can Liao, Kefei Ouyan, Shan Li, Changshu State and Local Joint Engineering Research Center for Satellite Navigation and Location Service Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9781728190457

When using the compressed sensing method in Synthetic Aperture Radar(SAR) imaging, there are two major problems: long calculation time and insufficient scalability of t calculation ability. In order to solve the above problems, this paper proposes a distributed imaging method for SAR compressed sensing imaging based on MapReduce. First, the sparse data is labeled, then the range and azimuth image are reconstructed by two MapReduce calculation processes. With parallel computing advantages, the acceleration of SAR compressed sensing imaging is realized. © 2020 IEEE.

关键词： MapReduce

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1st International conference of Advanced Computing and Informatics, ICACIN 2020

1st International Conference of Advanced Computing and Infor...

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1st International conference of Advanced Computing and Informatics, ICACIN 2020

ISBN: (纸本)9789811560477

The proceedings contain 56 papers. The special focus in this conference is on Advanced Computing and Informatics. The topics include: Evaluation of Several Artificial Intelligence and Machine Learning Algorithms for image Classification on Small Datasets;A Comprehensive Study of Dissemination and Data Retrieval in Secure VANET-Cloud Environment;toward to Autonomous Broker for Virtual Network Provisioning and Monitoring;comparative Study on Random Traveling Wave Pulse-Coupled Oscillator Algorithm of Energy-Efficient Wireless Sensor Networks;impact of Inspirational Motivation on Organizational Innovation (Administrative Innovation, Process Innovation, and Product Innovation);managing the Smart Grid in Free Market;an Haptic Display for Visually Impaired Pedestrian Navigation;GPU parallelization for Accelerating 3D Primitive Equations of Ocean Modeling;yemeni Paper Currency Recognition System Using Deep Learning Approach;tuning Hyper-Parameters of Machine Learning methods for Improving the Detection of Hate Speech;performance Comparison of Machine Learning Techniques in Identifying Dementia from Open Access Clinical Datasets;Clustering Analysis to Improve Web Search Ranking Using PCA and RMSE;preface;optimization of a Similarity Performance on Bounded Content of Motion Histogram by Using distributed Model;A parallel Global TFIDF Feature Selection Using Hadoop for Big Data Text Classification;robust and Accurate Method for Textual Information Extraction Over Video Frames;a Deep Learning Architecture for Profile Enrichment and Content Recommendation;Predictive Data Analysis Model for Employee Satisfaction Using ML Algorithms;a Novel Approach for Big Data Monetization as a Service;ensemble Feature Selection Method Based on Bio-inspired Algorithms for Multi-objective Classification Problem;automated Binary Classification of Diabetic Retinopathy by Convolutional Neural Networks.

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Effective Anomaly Detection Based on Reinforcement Learning in Network Traffic Data

Effective Anomaly Detection Based on Reinforcement Learning ...

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International conference on parallel and distributed Systems (ICPADS)

作者： Zhongyang Wang Yijie Wang Hongzuo Xu Yongjun Wang Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology

Mixed-type data with both categorical and numerical features are ubiquitous in network security, but the existing methods are minimal to deal with them. Existing methods usually process mixed-type data through feature conversion, whereas their performance is downgraded by information loss and noise caused by the transformation. Meanwhile, existing methods usually superimpose domain knowledge and machine learning in which fixed thresholds are used. It cannot dynamically adjust the anomaly threshold to the actual scenario, resulting in inaccurate anomalies obtained, which results in poor performance. To address these issues, this paper proposes a novel Anomaly Detection method based on Reinforcement Learning, termed ADRL, which uses reinforcement learning to dynamically search for thresholds and accurately obtain anomaly candidate sets, fusing domain knowledge and machine learning fully and promoting each other. Specifically, ADRL uses prior domain knowledge to label known anomalies and uses entropy and deep autoencoder in the categorical and numerical feature spaces, respectively, to obtain anomaly scores combining with known anomaly information, which are integrated to get the overall anomaly scores via a dynamic integration strategy. To obtain accurate anomaly candidate sets, ADRL uses reinforcement learning to search for the best threshold. Detailedly, it initializes the anomaly threshold to get the initial anomaly candidate set and carries on the frequent rule mining to the anomaly candidate set to form the new knowledge. Then, ADRL uses the obtained knowledge to adjust the anomaly score and get the score modification rate. According to the modification rate, different threshold modification strategies are executed, and the best threshold, that is, the threshold under the maximum modification rate, is finally obtained, and the modified anomaly scores are obtained. The scores are used to re-carry out machine learning to improve the algorithm's accuracy for anomalo

关键词： Knowledge engineering Machine learning algorithms conferences distributed databases Reinforcement learning Telecommunication traffic Network security

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Inline Double Layer Depth Estimation with Transparent Materials 42nd

Inline Double Layer Depth Estimation with Transparent Materi...

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42nd German conference on Pattern Recognition, DAGM GCPR 2020 held in parallel with 25th International Symposium on Vision, Modeling, and Visualization, VMV 2020 and 10th Eurographics Workshop on Visual Computing for Biology and Medicine, VCBM 2020

作者： Kopf, Christian Pock, Thomas Blaschitz, Bernhard Štolc, Svorad Graz University of Technology Graz Austria AIT Austrian Institute of Technology GmbH Vienna Austria Photoneo s.r.o. Bratislava Slovakia

ISBN: (纸本)9783030712778

3D depth computation from stereo data has been one of the most researched topics in computer vision. While state-of-art approaches have flourished over time, reconstruction of transparent materials is still considered an open problem. Based on 3D light field data we propose a method to obtain smooth and consistent double-layer estimates of scenes with transparent materials. Our novel approach robustly combines estimates from models with different layer hypotheses in a cost volume with subsequent minimization of a joint second order TGV energy on two depth layers. Additionally we showcase the results of our approach on objects from common inspection use-cases in an industrial setting and compare our work to related methods. © 2021, Springer Nature Switzerland AG.

关键词： Stereo image processing

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A High Performance Computing Method for Noise Cross-Correlation Functions of Seismic Data

A High Performance Computing Method for Noise Cross-Correlat...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Junwei Zhou Qian Wei Chao Wu Guangzhong Sun School of Earth and Space Sciences University of Science and Technology of China School of Physical Sciences University of Science and Technology of China Network and Information Center University of Science and Technology of China School of Computer Science and Technology University of Science and Technology of China

Calculation of noise cross-correlation functions (NCF) plays an important role in ambient noise imaging which is a vital seismic method to obtain Earth inner structures. To raise the resolution of the imaging results, we need more seismic data for imaging. However, as the size of seismic data increases, the serial algorithm for NCF calculation becomes much more time-consuming. Thus, how to accelerate the NCF calculation becomes a key problem in ambient noise imaging. Based on the analysis of serial algorithm, we proposed a new parallel algorithm for NCF calculation using NVIDIA GPU platform. In addition, we improved reading and writing strategy to reduce I/O consumption. Experimental results on real seismic data show the effectiveness of our method. The parallel program achives about 1861 times speedup.

关键词： Earth image resolution High performance computing Imaging Graphics processing units Writing parallel algorithms

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Automated Detection of Central Chest Lymph Nodes for Guided Bronchoscopy

Automated Detection of Central Chest Lymph Nodes for Guided ...

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2025 conference on Medical Imaging

作者： He, Yuxuan Higgins, William E. Penn State Univ Sch Elect Engn & Comp Sci University Pk PA 16802 USA

ISBN: (纸本)9781510685925;9781510685932

Physicians now commonly use guided bronchoscopy systems to help diagnose lung cancer in early stage patients. To use such a system, the physician draws on a patient's high-resolution chest computed tomography (CT) scan to preplan a bronchoscopic procedure for a peripheral nodule observed on the CT. Later, in the operating room, the physician uses the guidance system, driven by the procedure plan, to navigate the bronchoscope to the nodule and perform the desired tissue biopsy. Central-chest lymph node staging, however, is also required to ascertain a patient's disease stage. Unfortunately, current practice tends to rely on manually identifying only 2 or 3 enlarged lymph nodes (short axis >= 10 mm) on a thick-slice CT scan (slice spacing >= 3 mm). Yet, to have a truly accurate and effective assessment of disease stage, the staging procedure should satisfy two criteria: 1) consider a more comprehensive set of nodes distributed across the chest;2) include nodes smaller than what had heretofore been considered in clinical practice. Because manual identification of lymph nodes is known to be challenging, researchers have suggested automatic deep learning methods for CT-based lymph extraction. Unfortunately, these methods have only focused on defining enlarged lymph nodes observed in thick-slice CT scans, as typically done by radiologists. We present a three-stage framework that detects lymph nodes across a broader size range;i.e., short axis >= 5 mm;and draws on high-resolution chest CT scans used for guided bronchoscopy (slice spacing < 1 mm). The framework includes: 1) pre-processing, to reduce redundant information;2) model inference, using a trained Swin-UNETR Transformer model;and 3) post-prediction filtering, to eliminate false positives. On a 41-patient training and validation dataset containing 847 nodes having short axis >= 5 mm, our model showed an average sensitivity of 76.5% for lymph node detection. In a separate comparison test involving a set of 3D high-re

关键词： lung lymph node staging deep learning lymph node detection image-guided bronchoscopy CT imaging Swin-Unet transformer

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Comparison in Power-Forecasting methods for Geographically distributed PV Power Systems Using Their Previous Datasets 4

Comparison in Power-Forecasting Methods for Geographically D...

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4th International conference on Smart Grid and Smart Cities (ICSGSC)

作者： Kure, Taiki Miyazaki, Yosui Kondoh, Junji Kameda, Yusuke Tokyo Univ Sci Grad Sch Sci & Technol Dept Elect Engn Noda Chiba 2788510 Japan

ISBN: (数字)9781728194042

ISBN: (纸本)9781728194042

Power generation from photovoltaic (PV) power systems is increasing from the perspective of environmental sustainability. However, the amount of PV power generated is unstable because it depends on weather conditions. For this reason, accurate prediction of PV power is required by electric power suppliers, and various forecast methods have been proposed, such as methods using machine learning. This study uses another method that predicts the short-term output of geographically distributed PV systems from the optical flow which is one of image processing methods of normalized output power distribution by applying motion compensation prediction, which is a technology for estimation of motion images. In this paper, we compare the proposed method with the persistence forecast, and evaluate the effectiveness of the proposed method. As a result, the average MAPE of the proposed method was 4.62 %, whereas that of persistence forecast was 6.4 %, which indicates that the proposed method is an effective approach for PV power forecast.

关键词： PVpower forecast optical flow normalized output power distribution mean absolute percentage error

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IO Challenges for Human Brain Atlasing using Deep Learning methods - An in-depth analysis 27

IO Challenges for Human Brain Atlasing using Deep Learning M...

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27th Euromicro International conference on parallel, distributed and Network-Based processing (PDP)

作者： Oden, Lena Schiffer, Christian Spitzer, Hannah Dickscheid, Timo Pleiter, Dirk Forschungszentrum Julich JSC Julich Germany Forschungszentrum Julich Inst Neurosci & Med INM 1 Julich Germany Fernuniv Fak Math & Informat Hagen Germany

ISBN: (纸本)9781728116440

The use of Deep Learning methods have been identified as a key opportunity for enabling processing of extreme-scale scientific datasets. Feeding data into compute nodes equipped with several high-end GPUs at sufficiently high rate is a known challenge. Facilitating processing of these datasets thus requires the ability to store petabytes of data as well as to access the data with very high bandwidth. In this work, we look at two Deep Learning use cases for cytoarchitectonic brain mapping. These applications are very challenging for the underlying IO system. We present an in depth analysis of their IO requirements and performance. Both applications are limited by the IO performance, as the training processes often have to wait several seconds for new training data. Both applications read random patches from a collection of large HDF5 datasets or TIFF files, which result in many small non-consecutive accesses to the parallel file systems. By using a chunked data format or storing temporally copies of the required patches, the IO performance can be improved significantly. These leads to a decrease of the total runtime of up to 80%.

关键词： Training Task analysis Deep learning Graphics processing units Brain Bandwidth image resolution

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MPS: A Multiple Poisoned Samples Selection Strategy in Backdoor Attack

MPS: A Multiple Poisoned Samples Selection Strategy in Backd...

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IEEE International conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Weihong Zou Shigeng Zhang Weiping Wang Jian Zhang Xuan Liu Schoosl of Computer Science and Engineering Central South University Science and Technology on Parallel and Distributed Processing Laboratory (PDL) College of Computer Science and Electronic Engineering Hunan University

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Recently there has been many studies on backdoor attacks, which involve injecting poisoned samples into the training set in order to embed backdoors into the model. Existing multiple poisoned samples attacks usually randomly select a subset from clean samples to generate the poisoned samples. Filtering-and-Updating Strategy (FUS) has shown that the poisoning efficiency of each poisoned sample is inconsistent and random selection is not optimal. However, FUS does not fully considered the selection of multiple poisoned samples, there are still some issues with the selection of multiple poisoned samples. In this paper, we formulate the selection of multiple types of poisoned samples as a multi-objective optimization problem and proposed a Multiple Poisoned Samples Selection Strategy (MPS) to solve the issue. Unlike FUS, we consider the potential of clean samples that are not selected as to become efficient poisoned samples. Specifically, we use a weight-based contribution approach to calculate the contribution of each sample (clean sample and poisoned sample) during the training process from multiple dimensions. Finally, based on the greedy approach, we retain a subset of samples with the largest contribution in each dimension through iterations. We evaluate the effectiveness of MPS on various attack methods, including BadNet, Blended, ISSBA, and WaNet, as well as benchmark datasets. The experimental results on CIFAR-10 and GTSRB show that MPS can increase the attack strength by 1.45% to 18.34% compared to RSS and 0.43% to 10.84% compared to FUS in multiple poisoned samples attacks, thereby enhancing the stealthiness of the attack. Meanwhile, MPS is suitable for black-box settings, meaning that poisoned samples selected in one setting can be applied to other settings.

关键词： Training Data privacy Closed box Benchmark testing Security Protection Optimization

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