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检索条件"任意字段=International Congress on Image and Signal Processing, BioMedical Engineering and Informatics"

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Pedestrian detection based on Laplace operator image enhancement algorithm and Faster R-CNN 11

Pedestrian detection based on Laplace operator image enhance...

引用

11th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Tian, Qing Xie, GuangDa Wang, YanPing Zhang, Yuan North China Univ Technol Sch Elect & Informat Engn Beijing Peoples R China

ISBN: (纸本)9781538676042

Pedestrian detection is an important branch of computer vision. Many car manufactures have used this technology in real situation. Recently, deep learning has become the best method of pedestrian detection, and the advantage of deep neural network is that it can use statistical method to extract high-level features from raw sensory data and to obtain effective feature. Currently, Faster R-CNN is a typical framework, which commonly be used in the field of image processing. However, in order to achieve better performance in pedestrian detection, Faster R-CNN requires a large number of high-quality training samples. Due to the change of light and pedestrian density, the quality of the collected image is poor. Based on this problem, our research introduces Laplacian operator, it can enhance local image comparison. By introducing the Laplace operator, the proposed method can effectively preprocess the samples of the Faster R-CNN. The real data experiments verify the effectively of this algorithm as well as good robustness to the interference.

关键词： Laplacian operator image enhancement Faster R-CNN pedestrian detection

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Learning-Based Index-of-Maximum Hashing for Touch-Stroke Template Protection

Learning-Based Index-of-Maximum Hashing for Touch-Stroke Tem...

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international congress on image and signal processing, biomedical engineering and informatics

作者： Jinwei Zhi Shih Yin Ooi Andrew Beng Jin Teoh School of Electrical and Electronic Engineering Yonsei University Seoul South Korea

Touch-stroke dynamics is an emerging biometrics which captures a user's unique way of swiping a set of "strokes" on the mobile devices, and uses them as a matching template. Unexceptional to other biometrics, this template is also vulnerable to adversary attacks that may lead to privacy invasion and impersonation risk. One of the ideal ways to protect the biometric template is through the method of cancelable biometrics. On top of distorting the original template, it makes the template revocable as well. In this work, we propose an improved cancelable touch-stroke template method based on the Index-of-Maximum (IoM) hashing, and termed as learning based IoM (LIoM) hashing throughout this paper. Unlike vanilla data-agnostic IoM hashing, data-driven based LIoM hashing utilizes the supervising learning mechanism to generate a more discriminative and compact cancelable touch-stroke template. The proposed method achieved better performance than original template on Frank touch-stroke dynamic database with same feature length as well as satisfies three template protection design criteria, namely irreversibility, revocability and unlinkability.

关键词： Biometrics (access control) Authentication Training Hash functions Classification algorithms Databases Supervised learning

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Predicting image Emotion Distribution by Emotional Region 11

Predicting Image Emotion Distribution by Emotional Region

引用

11th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Fan, Yangyu Yang, Hansen Li, Zuhe Liu, Shu Northwestern Polytech Univ Sch Elect & Informat Xian Shaanxi Peoples R China Cent South Univ Sch Informat Sci & Engn Changsha Hunan Peoples R China Zhengzhou Univ Light Ind Sch Comp & Commun Engn Zhengzhou Henan Peoples R China

ISBN: (纸本)9781538676042

Recent studies on image emotion prediction mainly focus on classifying images into a certain emotion category, but single label cannot reflect peoples' multiple emotion to image. To get more realistic results, we study image emotion distribution problem. In the most image emotion tasks, features are extracted from the whole image, but not each part makes contribution to emotion, so features from the whole image contain noises. In order to get discriminative features, we propose to leverage the heatmap generated by Fully Convolutional Networks (FCN) to select the Region of Interest (ROI) from an image which represents the image emotion most. Both high-level features and hand-crafted features from ROI are fused to train Support Vector Regressors (SVRs) to predict emotion distribution. Extensive experiments conducted on two widely used datasets demonstrate that emotional region is selected out through our method so that emotion distribution prediction performances are improved.

关键词： image emotion distribution FCN emotional region SVR

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Constructing a Comprehensive Clinical Database Integrating Patients' Data from Intensive Care Units and General Wards

Constructing a Comprehensive Clinical Database Integrating P...

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international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Tongbo Liu Xiaoli Liu Yong Fan Haoran Xu Yeuk Lam Ng Peiyao Li Wanguo Xue Zhengbo Zhang Chinese PLA General Hospital Beijing China School of Biological Science and Medical Engineering Beihang University Beijing China Faculty of Arts & Science University of Toronto Toronto Canada Medical Big Data Center Chinese PLA General Hospital Beijing China

Collection and analysis of large volumes of ICU data are invaluable to the advancement of clinical knowledge, and large-scale ICU databases have been effective resources to understand risk factors and perform predictive analysis by using machine learning. This paper introduces the construction of a comprehensive clinical database: PLAGH-ICU database, which integrated data from nine intensive care units, emergency department and general wards. Data from several sources were extracted and integrated in the database, including patient demographics, hospital administrative data, physiological data, medications, lab test, fluid balance data, notes and reports etc. Detailed information about the database, such as patient characteristics, disease distribution, category of data, data records were illustrated. As far as we know, this is the first comprehensive ICU database developed and used as a research database in datathon event in China. Such kind of database will promote the research progress in critical care medicine, as well as the development of modern hospital information system in China.

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Awareness and Cooperation in Neural Network Ensemble Learning

Awareness and Cooperation in Neural Network Ensemble Learnin...

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international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Yong Liu School of Computer Science and Engineering The University of Aizu Aizu-Wakamatsu Fukushima Japan

It is important to create a set of cooperative learning modules in order to combine them into an ensemble learning machine. If a data point has already been learned by the ensemble learning system, some individual learners in the ensemble should know how to react by either stopping learning further on the learned data, or forgetting the learned data. Such awareness would be essential in keeping each individual learner to be a specialist and cooperative to other individual learners in the same ensemble learning system. In this paper, each individual learning machine gains awareness through adjusting learning directions and learning strength in learning each data by negative correlation learning with selection (NCLS). If the data had not been learned by the ensemble system, each individual learning module in the ensemble would adopt the common learning strategies in which the weight learning would lead the module's output to be close to the target output. However, if the data had been learned by the ensemble system, each individual learning module would tend to go to the opposite learning direction, or weaken the learning strength in order to learn to be different to the rest of the learning modules in the ensemble. Simulation results have been shown how such mixed learning strategies could build awareness among the individual learning modules in the ensemble.

关键词： Training Neural networks Learning systems Correlation Task analysis Error analysis

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Siamese Score: Detecting Mode Collapse for GANs

Siamese Score: Detecting Mode Collapse for GANs

引用

international congress on image and signal processing, biomedical engineering and informatics

作者： Jizheng Jia Qiyang Zhao School of Computer and Science and Engineering Beihang university Beijing China

Despite large strides in terms of generative adversarial networks (GANs) for image generation, evaluating and comparing GANs remains an open question. Several measures have been introduced, however, there is no consensus in terms of the best score. In this paper, we delve into the widely-used metric Inception Score (based on KL divergence), revealing that it fails to detect intra-class mode collapse. Meanwhile, Wasserstein distance has received much attention in comparing distributions in recent years but suffers heavy computational burden in high dimensional space. Our idea is that we can find specific embedding space where Euclidean distance could mimic Wasserstein distance to solve the heavy computational problem. This space can be found using a Siamese network, which could be trained quickly because of shared weights. We also apply several proposed new techniques to get better image embedding. To evaluate our proposed metric (Siamese Score), we simulate mode collapse using K-means clustering performed on real data set. To further validate it, we perform an empirical study on several GAN models and use the generated images to do the task. Experiments show that Siamese Score can detect mode collapse and is time-efficient compared with Inception Score and we think our score can be complementary to Inception Score.

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Enhancing the Representation of Multiple Anatomical Network for Young Adults with Self-Esteem Difference 12

Enhancing the Representation of Multiple Anatomical Network ...

引用

12th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2019

作者： Peng, Bo Saxena, Aditya Wang, Suhong Dai, Yakang Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of Sciences Suzhou China Trauma Center Khandwa District Hospital Khandwa India Third Affiliated Hospital Soochow University Department of Neuroscience Changzhou China Suzhou Key Laboratory of Medical and Health Information Technology Suzhou China Nanjing Guoke Medical Engineering Technology Development co. LTD Nanjing China Jinan Guoke Medical Engineering Technology Development co. LTD Jinan China

ISBN: (纸本)9781728148526

Anatomical brain networks help explain how distributed brain area are connected, and the connectivity differences have been used to recognize neuroimaging biomarkers for mental and psychologic symptoms. However, most methods analyzing anatomical brain networks employ single-level network framework, which cannot give an overall representation on the subtle deviations of the whole brain. In this study, we put forward an enhancing representation of multiple anatomical networks for young adults with self-esteem differences. Specifically, we construct a four-layer network framework using both morphological features (i.e. gray matter volume, cortical thickness) for each region of interest (ROI) and hierarchical networks features. Two types of filter-based approaches are adopted, followed by a wrapper-based approach to select a set of distinctive features. Then, multi-kernel machine learning method is used to combine two kinds of features (ROI-based morphological features and hierarchical correlative features) into a single classifier. Our method achieves an accuracy of 97.26%, which has an outstanding performance compared with the single-network methods. This study gives a potential evidence that self-esteem is related to specific ROIs and anatomical brain connections. © 2019 IEEE.

关键词： Magnetic resonance imaging

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Gait Phase Discrimination Based on a Foot-Mounted Gyroscope

Gait Phase Discrimination Based on a Foot-Mounted Gyroscope

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international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Wenjian Wang Fangzhou Hu Yi Zhang Renjian Feng Yinfeng Wu Zhanjun Wang Changcheng Institute of Metrology and Measurement Beijing China School of Instrumentation and Optoelectronic Engineering Beihang University Beijing China Capital Aerospace Machinery Company Beijing China

Gait phase discrimination is the temporal gait parameters analyzing issue. In most gait phase discrimination algorithm based on the foot-mounted gyroscope, thresholds are used to detected gait events, which is detrimental to the robustness of detection results. In order to improve the adaptability of the detection of gait events (especially heel strike and toe off), this paper proposes and evaluates a new segmented dynamic threshold method based on local peak detection for gait phase discrimination using the pitch angular velocity. Four typical gait events including heel strike (HS), toe strike (TS), heel off (HO) and toe off (TO) are all detected. In addition, we detect two new gait events, including the top position of backswing (BT) and the top position of forward swing (FT). The results of experiment show that the mean time differences of the detection between the presented algorithm and the reference system are 48.83 ± 4.97ms for HS, 31.12 ± 11.18ms for TS, -65.37 ± 21.35ms for HO and -31.06 ± 12.37ms for TO, respectively. Furthermore, the proposed algorithm can detect HS and TO efficiently at the different walking speed.

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3D Convolutional Networks Based Automatic Diagnosis of Alzheimer's Disease Using Structural MRI

3D Convolutional Networks Based Automatic Diagnosis of Alzhe...

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international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Xinying Yu Bo Peng Jun Shi Jianbin Zhu Yakang Dai Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of Sciences Suzhou China Shanghai Institute for Advanced Communication and Data Science School of Communication and Information Engineering Shanghai University China Suzhou Science & Technology Town Hospital Suzhou Jiangsu Province China Jinan Guoke Medical Engineering Technology Development co. LTD Jinan China

Early diagnosis of Alzheimer's disease (AD) can help take timely treatment before patients suffer from irreversible brain damage. However, current studies using manually feature extraction and 2D CNN methods cannot make the classification performance optimized. We proposed an approach using 3D convolutional neural network (CNN) to diagnose AD and mild cognitive impairment (MCI), which is the prodromal stage of AD, from structural magnetic resonance images. Subjects of 150 AD, 129 MCI, and 112 normal controls (NC) from the Alzheimer's Disease Neuroimaging Initiative dataset were used in this study. Specifically, weights of a 3-class network were trained to initialize the 2-class network and fine-tuned, which saves time and avoids overfitting. The experimental results show that our 3D-sVoxCNN model significantly improves the classification performance with the accuracy of 95.05% (AD vs MCI vs NC), 99.47% (AD vs. NC), 98.32% (MCI vs. NC), and 98.32% (AD vs. MCI).

关键词： Three-dimensional displays Convolution Solid modeling Diseases Feature extraction Magnetic resonance imaging biomedical imaging

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Improved Automatic Radiographic Bone Age Prediction with Deep Transfer Learning

Improved Automatic Radiographic Bone Age Prediction with Dee...

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international congress on image and signal processing, biomedical engineering and informatics

作者： Wei Tang Gang Wu Gang Shen School of Software Engineering Tongji Hospital Huazhong University of Science and Technology Wuhan China

Radiograph based pediatric bone age assessment plays a key role in many medical and judiciary tasks. The traditional bone age identification techniques, such as GP method and TW scoring, rely on human experts to compare X-ray image of the hand skeleton with the standard atlas and visually examine the similarities, thus impacted by an individual's experience and judgment. The recent advances of deep neural networks (DNN) have made possible more accurate bone age estimation with the help of computer vision representation and understanding. DNNs usually demands large scale annotated image datasets for training, but this is not guaranteed for bone age estimation because of the incurred cost and the concerns of privacy protection. In this paper, we report our work of developing a convolutional neural network (CNN) based bone age classifier and transferring the low level hand radiograph features learned from the Radiological Society of North America (RSNA) dataset to the small dataset collected from a local hospital. The first few layers of the proposed network are treated as the feature extractors while the remaining parts are further fine-tuned to improve the transfer performance. The experiment results show that the proposed transfer learning framework achieves a mean absolute difference (MAD) of 0.16 years, outperforming other state-of-the-art algorithms.

关键词： Bones X-ray imaging Feature extraction biomedical imaging Training Pediatrics Neural networks

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