研究者詳細 - 神谷　亨

論文 - 神谷　亨

分割表示757 件中 241 - 260 件目／全件表示 >>

Marine Organisms Tracking and Recognizing Using YOLO 査読有り国際誌

Uemura T., Lu H., Kim H.

EAI/Springer Innovations in Communication and Computing 53 - 58 2020年01月

担当区分：責任著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

A system that investigates deep sea automatically has never developed. A purpose of this study is developing such a system. We employed a technique of recognition and tracking of multi-objects, called “You Only Look Once: YOLO.” This method provides us very fast and accurate tracker. In our system, we remove the haze, which is caused by turbidity of water, from image. After its process, we apply “YOLO” to tracking and recognizing the marine organisms, which includes shrimp, squid, crab, and shark. Our developed system shows generally satisfactory performance.

DOI： 10.1007/978-3-030-17763-8_6

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85097568303&origin=inward

pix2surv: A Generative Adversarial Network Model for Prediction of Survival in Patients with Interstitial Lung Diseases 査読有り国際誌

Uemura T, Watari C, Näppi JJ, Hironaka T, Kim H, Yoshida H

Radiological Society of North America 2019 Scientific Assembly and Annual Meeting 2019年12月

YOLOｖ２を用いた海中生物のロバスト検出招待有り査読有り

陸、酒井、金

画像ラボ 30 ( 12 ) 21 - 25 2019年12月

Artificial intelligence (AI) for CT colonography: the new horizons of colorectal screening 査読有り国際誌

Näppi JJ, Uemura T, Pickhardt PJ, Kim H, Yoshida H.

Radiological Society of North America 2019 Scientific Assembly and Annual Meeting 2019年12月

Generative adversarial network models for prediction of survival in patients with interstitial lung diseases 査読有り国際誌

Uemura T, Watari C, Näppi JJ, Hironaka T, Kim H, Yoshida H

Radiological Society of North America 2019 Scientific Assembly and Annual Meeting 2019年12月

Touch switch sensor for cognitive body sensor networks 査読有り国際誌

Li Y., Lu H., Kim H., Serikawa S.

Computer Communications 146 32 - 38 2019年10月

記述言語：英語掲載種別：研究論文（学術雑誌）

© 2019 Elsevier B.V. With the global popularity of Internet of Things (IoT) technology, increasingly numbers of digital mobile products have been developed, and they have increased the productivity of people's daily lives. These electronic products are used in all aspects of life, such as medical care, office life, home services, and sports. However, most of these products are designed for healthy people with high literacy rates. For disabled people, these products cannot be widely used. In this paper, new, differently shaped touch sensors are proposed for body sensor network-based devices. This touch sensor can be formed into any shape because of the use of conductive fabric adhesive tape as a switch. That property is why the sensor can change positions in the body sensor network in which the human body is used as a trigger to safely activate the touch switch. The number of switch sensors can easily be increased or decreased without changing the wiring of the central controller. The number of sensors in a switch sensor system is greater than that in other touch switch systems, and the accuracy is higher.

DOI： 10.1016/j.comcom.2019.07.019

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85069818135&origin=inward

A Detection Method for Liver Cancer Region Based on Faster R-CNN 査読有り

Furuzuki M., Lu H., Kim H., Hirano Y., Mabu S., Tanabe M., Kido S.

International Conference on Control, Automation and Systems 2019-October 808 - 811 2019年10月

担当区分：責任著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

© 2019 Institute of Control, Robotics and Systems - ICROS. In recent years, liver cancer has become the fourth-largest number of deaths in the world. Surgery is a typical treatment for liver cancer. Therefore, advance information about the number and size of cancer is important for surgery. Multi-phase CT images are well known diagnostic method. By extracting the region of the liver and the region of cancer from the obtained CT image, the shape can be finally restored in 3D. In this paper, as a preliminary step to construct an image analysis method for efficiently extracting cancerous regions in multi-phase CT, we propose a method of obtaining a rectangular region as a rough cancerous region of interest. As a method, after preprocessing the input image, using Faster R-CNN, the region of interest including the cancer region is extracted as a rectangle. As a result of applying this method to 11 cases of arterial phase of multi-phase CT, the detection performance was different depending on the network model adopted for backbone part.

DOI： 10.23919/ICCAS47443.2019.8971627

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85079102007&origin=inward

Segmentation of Bone Metastasis in CT Images Based on Modified HED 査読有り国際誌

Song Y., Lu H., Kim H., Murakami S., Ueno M., Terasawa T., Aoki T.

International Conference on Control, Automation and Systems 2019-October 812 - 815 2019年10月

担当区分：責任著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

© 2019 Institute of Control, Robotics and Systems - ICROS. Segmentation of the bone metastasis area in medical images can reduce the workload for diagnosis and treatment. However, there are various shapes and sizes of bone metastasis also affected by noise. As a result, it is difficult to segment using classical segmentation methods. In this paper, we propose a convolutional neural network model-based segmentation method. The proposed method easily predicts the contour and location of the lesion area using side connection and modified network. In this study, we modified again the modified HED network to match the characteristics of bone metastasis. The experimental results using the proposed method for segmenting bone metastasis in the lesion area has 79.8[%] of TP rate and 69.2[%] of IOU rate.

DOI： 10.23919/ICCAS47443.2019.8971539

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85079084833&origin=inward

Recognition of Surrounding Environment for Electric Wheelchair Based on WideSeg 査読有り国際誌

Sakai Y., Nakayama Y., Lu H., Li Y., Kim H.

International Conference on Control, Automation and Systems 2019-October 816 - 820 2019年10月

担当区分：責任著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

© 2019 Institute of Control, Robotics and Systems - ICROS. At present, the aging population is growing in Japan. Along with that, the expectation for the utilization of welfare equipment is increasing. Electric wheelchair, a convenient transportation tool, is popularized rapidly. On the other hand, accidents have occurred, and the dangers for driving are pointed out. Therefore, it needs to improve accident factors, reduce accidents and improve the convenience of electric wheelchair by automation. Environmental recognition is necessary for the development of autonomous electric wheelchair. Environmental recognition includes self-position estimation, recognition of sidewalks, crosswalks and traffic lights, moving object prediction, etc. In order to solve these various problems, this paper examines the segmentation of sidewalks, crosswalks and traffic lights. We develop the WideSeg that is one of semantic segmentation algorithms applying convolutional neural networks (CNN).

DOI： 10.23919/ICCAS47443.2019.8971608

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85079103064&origin=inward

Detection of Lung Nodules from Temporal Subtraction Image Using Deep Learning 査読有り国際誌

Tamai K., Miyake N., Lu H., Kim H., Murakami S., Aoki T., Kido S.

International Conference on Control, Automation and Systems 2019-October 1033 - 1036 2019年10月

担当区分：責任著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

© 2019 Institute of Control, Robotics and Systems - ICROS. In recent years, the number of death due to lung cancer is increasing year by year worldwide. Early detection and early treatment of lung cancer are important. Especially, early detection of the abnormalities on thoracic MDCT images detection of small nodules is required in visual screening. Although a CT apparatus is used for the examination, the burden on the image interpretation doctor is large due to the high performance of the CT, so the diagnostic accuracy may be reduced. In this paper, we propose an image analysis method to detect abnormal shadows from chest CT images automatically. The initial lesion candidate areas are extracted by using temporal subtraction technique that emphasizes temporal change by subtracting from a current image to previous one which is obtained same subject. The image of the area is given as input and classification is performed by CNN (Convolutional Neural Network). In the discrimination experiment based on our proposed method, 90.26 [%] of true positive rates and 13.58 [%] of false positive rates are obtained from the 49 clinical cases.

DOI： 10.23919/ICCAS47443.2019.8971533

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85079099157&origin=inward

Detection of Facial Symmetric Plane Based on Registration of 3D Point Cloud 査読有り国際誌

Hosoki D., Lu H., Kim H., Kimura N., Okawachi T., Nozoe E., Nakamura N.

International Conference on Control, Automation and Systems 2019-October 1037 - 1041 2019年10月

担当区分：責任著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

© 2019 Institute of Control, Robotics and Systems - ICROS. Cleft lip is a birth defect that occurs when the lips are not completely formed during healing of the face of the fetus. In Japan, it occurs in about 1 in 500 people. Although treatment is performed to form a symmetrical outer nose, it is necessary to evaluate the degree of symmetry of the surgical site quantitatively because it is based on the judgment criteria that depend on the doctor's subjectivity. In this paper, we propose a method to detect the plane which is the symmetry basis of the face to analyze the symmetry of the operation site. In the proposed method, the face organ is detected as points from 3D point cloud of the face. Then, the mirror image inversion excluding the area affected by shape change due to cleft lip is aligned with the original point cloud. Next, a symmetric plane is set by finding a plane that bisects vertically between one point in the original point cloud and the corresponding point in the mirror image. As a result of applying the proposed method to real 3D point cloud, we could detect the symmetric plane with good accuracy.

DOI： 10.23919/ICCAS47443.2019.8971537

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85079096298&origin=inward

Classification of Circulating Tumor Cells in Fluorescence Microscopy Images Based on SqueezeNet 査読有り国際誌

Nakamichi K., Lu H., Kim H., Yoneda K., Tanaka F.

International Conference on Control, Automation and Systems 2019-October 1042 - 1045 2019年10月

担当区分：責任著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

© 2019 Institute of Control, Robotics and Systems - ICROS. Circulating Tumor Cells (CTC) is expected as a useful biomarker test that can evaluate cancer metastasis. CTC exists in the blood of cancer patients and is considered to be an incentive of cancer metastasis. Pathologists analyze the blood to find these metastasis cancers from three colors of fluorescence microscopy images, but the manual analysis is time-consuming. In this paper, we develop an automatic CTC classification method in fluorescence microscopy images to reduce the burden of pathologists. In the proposed method, we detect cell regions by the bacterial foraging-based edge detection (BFED) algorithm and classify CTC by SqueezeNet, which is the kind of convolutional neural network (CNN). We apply the proposed method to 5040 microscopy images (6 samples) and evaluate the effectiveness. The experimental results demonstrate that the proposed method has a true positive rate is 89.86% and a false positive rate is 3.27%.

DOI： 10.23919/ICCAS47443.2019.8971646

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85079092431&origin=inward

Automatic Segmentation Method of Phalange Regions Based on Residual U-Net and MSGVF Snakes 査読有り国際誌

Kawagoe K., Hatano K., Murakami S., Lu H., Kim H., Aoki T.

International Conference on Control, Automation and Systems 2019-October 1046 - 1049 2019年10月

担当区分：責任著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

© 2019 Institute of Control, Robotics and Systems - ICROS. Bone diseases include rheumatoid arthritis and osteoporosis. Although visual screening using computed radiography (CR) images is an effective method for diagnosing osteoporosis, there are some similar diseases that exhibit low bone mass status. To this end, we aim to develop a computer-aided diagnostic (CAD) system to support the automatic diagnosis of osteoporosis from CR images. In this paper, we use convolutional neural network (CNN) and multiscale gradient vector flow snakes (MSGVF Snakes) algorithms to segment each finger bone regions from the CR image. The proposed method is applied to 15 cases, 92.95 [%] of the true positive rates, 2.21 [%] of the false positive rates, 7.05 [%] of the false negative rates are obtained respectively.

DOI： 10.23919/ICCAS47443.2019.8971740

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85079102296&origin=inward

Automatic Classification of Large-Scale Respiratory Sound Dataset Based on Convolutional Neural Network 査読有り国際誌

Minami K., Lu H., Kim H., Mabu S., Hirano Y., Kido S.

International Conference on Control, Automation and Systems 2019-October 804 - 807 2019年10月

担当区分：責任著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

© 2019 Institute of Control, Robotics and Systems - ICROS. Auscultation of respiratory sounds is very important for discovering the respiratory disease. However, there is no quantitative evaluation method for the diagnosis of respiratory sounds until now. It is necessary to develop a system to support the diagnosis of respiratory sounds. In addition, there are few studies using dataset suitable for generating realistic classification models that can be used in clinical sites in algorithm development for automatic analysis of respiratory sounds. We describe the development of an algorithm for the automatic classification of the large-scale respiratory sound dataset used in ICBHI 2017 Challenge as containing crackles, containing wheeze, containing both, and normal. Our approach consists of two major components. Firstly, transformation of one-dimensional signals into two-dimensional time-frequency representation images using short-time Fourier transform and continuous wavelet transform. Secondly, classification of transferred images using convolutional neural networks. In this paper, we apply our proposed method to 920 respiratory sound data, and achieve score of 28[%], harmonic score of 81[%], sensitivity of 54[%] and specificity of 42[%].

DOI： 10.23919/ICCAS47443.2019.8971689

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85079096950&origin=inward

3D-DenseNetによるCT Colonographyにおける擬陽性陰影の低減査読有り

植村、Janne、陸、橘、弘中、金、吉田

第18回情報科学技術フォーラム第2分冊 387 - 388 2019年09月

SqueezeNetを用いた顕微鏡画像からの血中循環がん細胞の自動識別査読有り

中道、陸、金、米田、田中

第18回情報科学技術フォーラム第2分冊 391 - 394 2019年09月

敵対的生成ネットワークによる間質性肺疾患患者の予後予測査読有り

植村、渡利、Janne、弘中、金、吉田

第18回情報科学技術フォーラム第2分冊 389 - 390 2019年09月

Convolutional neural network (CNNs) based image diagnosis for failure analysis of power devices 査読有り国際誌

Watanabe A., Hirose N., Kim H., Omura I.

Microelectronics Reliability 100-101 2019年09月

担当区分：責任著者記述言語：英語掲載種別：研究論文（学術雑誌）

© 2019 Elsevier Ltd An image diagnosis by deep learning was applied to failure analysis of power devices. A series of images during a process to failure by power cycling test was used for this method. The images were obtained by a scanning acoustic microscopy of our real-time monitoring system. An image classifier was designed based on a convolutional neural network (CNNs). A developed classifier successfully diagnosed input image into a normal device and an abnormal device. The accuracy of classification was improved by introducing a pre-training and an overlapping pooling into the system. A technique to extract a feature related a failure is essential for the failure analysis based on the real-time monitoring and the deep learning is one likely candidate for it.

DOI： 10.1016/j.microrel.2019.113399

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85074484253&origin=inward

Multi-organ segmentation from abdominal CT with random forest based statistical shape model 査読有り国際誌

Wu J., Li G., Lu H., Kim H.

ACM International Conference Proceeding Series 67 - 70 2019年08月

担当区分：責任著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

© 2019 Association for Computing Machinery. An automatic multi-organ segmentation method from upper abdominal CT image is proposed in this paper. A group of statistical shape models for multiple organs are generated by learning the statistical distribution of organs' shapes and intensity profiles. Then, a random forest regression model is trained to find the candidate position to initialize the statistical shape model. The proposed method is evaluated at segmentation of four abdomen organs (spleen, right kidney, left kidney and liver) from training set of 26 cases of upper abdominal CT images. The accuracy shows that the initialization improves the accuracy for statistical shape model-based segmentation.

DOI： 10.1145/3354031.3354042

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85073475971&origin=inward

Preface 査読有り

Kim H.

ACM International Conference Proceeding Series 2019年08月

　前のページ - 次のページ　

<神谷 亨>

論文 - 神谷 亨

<神谷　亨>

論文 - 神谷　亨