研究者詳細 - 神谷　亨

論文 - 神谷　亨

分割表示751 件中 261 - 280 件目／全件表示 >>

3次元点群の位置合わせによる顔の対称面検出

細木　大祐, 陸, 金, 木村, 大河内, 野添, 中村

第38回日本医用画像工学会大会予稿集 599 - 605 2019年07月

3D-CNN による経時的差分像上の結節状陰影検出査読有り

芳野由利子, 陸慧敏, 金亨燮, 村上誠一, 青木隆敏, 木戸尚治

医用画像情報学会雑誌 ( 医用画像情報学会 ) 36 ( 2 ) 77 - 82 2019年06月

担当区分：責任著者記述言語：日本語掲載種別：研究論文（学術雑誌）

<p>A temporal subtraction image is obtained by subtracting a previous image, which are warped to match between the structures of the previous image and one of a current image, from the current image. The temporal subtraction technique removes normal structures and enhances interval changes such as new lesions and changes of existing abnormalities from a medical image. However, many artifacts remain on a temporal subtraction image and these can be detected as false positives on the subtraction images. In this paper, we propose a 3D-CNN after initial nodule candidates are detected using temporal subtraction technique. To compare the proposed 3D-CNN, we used 7 model architectures, which are 3D ShallowNet, 3D-AlexNet, 3D-VGG11, 3D-VGG13, 3D-ResNet8, 3D-ResNet20, 3D-ResNet32, with these performance on 28 thoracic MDCT cases including 28 small-sized lung nodules. The higher performance is showed on 3D-AlexNet.</p>

DOI： 10.11318/mii.36.77

CiNii Article

CiNii Research

その他リンク： https://search.jamas.or.jp/link/ui/2019298826

3D convolutional neural networks for automatic detection of lung nodules from temporal subtraction images 査読有り

Yoshino, Lu, Kim, Murakami, Aoki, Kido

International Journal of Computer Assited Radiology and Surgery S71 - S72 2019年06月

Reduction of false-positive polyp detections in CT colonography using multiscale 3D residual networks 査読有り

Uemura, Janne J. Näppi, Lu, Kim, Tachibana, Hironaka, Yoshida

International Journal of Computer Assited Radiology and Surgery S67 - S68 2019年06月

3D registration method based on intensity gradient information for temporal subtraction technique in thoracic MDCT image 査読有り

Miyake, Lu, Kim, Murakami, Aoki, Kido

nternational Journal of Computer Assited Radiology and Surgery S22 - S23 2019年06月

CONet: A Cognitive Ocean Network 査読有り国際誌

Lu H., Wang D., Li Y., Li J., Li X., Kim H., Serikawa S., Humar I.

IEEE Wireless Communications 26 ( 3 ) 90 - 96 2019年06月

記述言語：英語掲載種別：研究論文（学術雑誌）

© 2019 IEEE. The scientific and technological revolution of the Internet of Things has begun in the area of oceanography. Historically, humans have observed the ocean from an external viewpoint in order to study it. In recent years, however, changes have occurred in the ocean, and laboratories have been built on the sea floor. Approximately 70.8 percent of the Earth's surface is covered by oceans and rivers. The Ocean of Things is expected to be important for disaster prevention, ocean resource exploration, and underwater environmental monitoring. Unlike traditional wireless sensor networks, the Ocean Network has its own unique features, such as low reliability and narrow bandwidth. These features will be great challenges for the Ocean Network. Furthermore, the integration of the ocean network with artificial intelligence has become a topic of increasing interest for oceanology researchers. The cognitive ocean network (CONet) will become the mainstream of future ocean science and engineering developments. In this article, we define the CONet. The contributions of the article are as follows: a CONet architecture is proposed and described in detail; important and useful demonstration applications of the CONet are proposed; and future trends in CONet research are presented.

DOI： 10.1109/MWC.2019.1800325

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85061026040&origin=inward

畳み込みニューラルネットワークを用いた指骨CR画像からの骨粗しょう症の識別査読有り

畠野、村上、植村、陸、金、青木

医用画像情報学会雑誌 ( 医用画像情報学会雑誌 ) 36 ( 2 ) 72 - 76 2019年06月

３D-CNNによる経時的差分像上の結節状陰影検出査読有り

芳野、陸、金、村上、青木、木戸

医用画像情報学会雑誌 36 ( 2 ) 77 - 82 2019年06月

The Cognitive Internet of Vehicles for Autonomous Driving 査読有り国際誌

Lu H., Liu Q., Tian D., Li Y., Kim H., Serikawa S.

IEEE Network 33 ( 3 ) 65 - 73 2019年05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）

© 1986-2012 IEEE. As it combines AI and IoT, autonomous driving has attracted a great deal of attention from both academia and industry because of its benefits to the economy and society. However, ultra-low delay and ultra-high reliability cannot be guaranteed by individual autonomous vehicles with limited intelligence and the existing architectures of the Internet of Vehicles. In this article, based on a cloud/fog-computing pattern and the IoT AI service framework, we propose a cross-domain solution for auto-driving. In contrast to existing studies, which mainly focus on communication technologies, our solution achieves intelligent and flexible autonomous driving task processing and enhances transportation performance with the help of the Cognitive Internet of Vehicles. We first present an overview of the enabling technology and the architecture of the Cognitive Internet of Vehicles for autonomous driving. Then we discuss the autonomous driving Cognitive Internet of Vehicles specifically from the perspectives of what to compute, where to compute, and how to compute. Simulations are then conducted to prove the effect of the Cognitive Internet of Vehicles for autonomous driving. Our study explores the research value and opportunities of the Cognitive Internet of Vehicles in autonomous driving.

DOI： 10.1109/MNET.2019.1800339

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85068858755&origin=inward

Low-light underwater image enhancement for deep-sea tripod 査読有り国際誌

Yujie Li, Jianru Li, Yun Li, H KIM, S SERIKAWA

IEEE Access 7 44080 - 44086 2019年04月

Ensemble 3D residual network(E3D-ResNet) for reduction of false-positive polyp detections in CTcolonography 査読有り

Tomoki Uemura, Janne J. Näppi, Huimin Lu, Hyoungseop Kim, Rie Tachibana, Toru Hironaka, Hiroyuki Yoshida

Proc. SPIE 10950, Medical Imaging 2019 1095013-1 - 1095013-7 2019年03月

対称性解析に基づく３次元データから顔の対称面の検出

細木、陸、金、木村、大河内、野添、中村

信学技報、IE2018-158 285 - 286 2019年03月

対称性解析に基づく口唇裂３次元点群からの正中面抽出法

山田、陸、金、木村、大河内、野添、中村

信学技報、IE2018-158 287 - 290 2019年03月

経時的差分像上の関心領域内の統計的特徴量に基づく結節状陰影の自動検出

金，田中，陸，村上，青木，木戸

電子情報通信学会総合大会 108 - 108 2019年03月

YOROを用いた海中生物のロバスト自律追跡

陸、金

動的画像処理実用化ワークショップ 65 - 66 2019年03月

Clinical Applications of Multidisciplunary Computational Anatomy to Diagnosis

Kido, Hashimoto, Hirano, Mabu, Kim, Kumura, Noriki, Inai, Tachibana

The 5th International Symposium on Mutidisciplinary Computational Anatomy 202 - 212 2019年03月

Recognition of surrounding environment from electric wheelchair videos based on modified YOLOv2 査読有り国際誌

Sakai Y., Lu H., Tan J., Kim H.

Future Generation Computer Systems 92 157 - 161 2019年03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）

© 2018 Elsevier B.V. Currently, the aging population is growing in Japan, and the needs for the utilization of welfare equipment are consequently increasing. The electric wheelchair, which is a convenient transportation tool, has rapidly become popular. However, many accidents have occurred when using electric wheelchairs, and the dangers of driving have been noted. Therefore, there is a need to improve accident factors, reduce accidents and improve the convenience of electric wheelchairs by using automation. Environmental recognition is the key technology for developing autonomous electric wheelchairs. Environmental recognition includes self-position estimation, the recognition of sidewalks, crosswalks and traffic lights, and moving object predictions. To solve these problems, this paper develops a system for detecting sidewalks, crosswalks and traffic lights. We develop the object recognition methods using a modified YOLOv2, which is an object detection algorithm that applies convolutional neural networks (CNNs). We detect the object through YOLOv2 and perform processing steps, such as unnecessary bounding box deletion and interpolation. The experimental results demonstrate that the average AUC of the detection rate is 0.587.

DOI： 10.1016/j.future.2018.09.068

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85054855462&origin=inward

CNNを用いた指骨CR画像からの骨粗しょう症の自動識別査読有り

畠野、村上、植村、陸、金、青木

Medical Imaging Technology 37 ( 2 ) 107 - 115 2019年03月

Dust removal from high turbid underwater images using convolutional neural networks 査読有り国際誌

Li Y., Zhang Y., Xu X., He L., Serikawa S., Kim H.

Optics and Laser Technology 110 2 - 6 2019年02月

記述言語：英語掲載種別：研究論文（学術雑誌）

© 2017 Elsevier Ltd In recent years, underwater image processing has been a focus of many studies. Most underwater image processing technologies are focused on descattering, absorption correction and reflection correction. For a deep sea mining machine, dust seriously affects visual acuity. To correct for this, a two-part dust removal approach is proposed. Underwater red-green minimum channel prior descattering is used to remove fine dust in the first stage. However, the impact of dust streaks on images is always undesirable. Consequently, we propose a further deep convolutional neural-network-based dust removal method. The experimental results show that the proposed method performs better in removing haze-like scatters and dust-like scatters.

DOI： 10.1016/j.optlastec.2017.09.017

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85029658908&origin=inward

Statistical shape model building method using surface registration and model prototype 査読有り国際誌

Li G., Wu J., Xiao Z., Kim H., Ogunbona P.

Optics and Laser Technology 110 234 - 238 2019年02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）

© 2017 Elsevier Ltd Automatic segmentation of organs from medical images is indispensable for the computer-assisted medical applications. Statistical Shape Models (SSMs) based scheme has been developed as an accurate and robust approach for extraction of anatomical structures, in which a crucial step is the need to place the sampled points (landmarks) with well corresponding across the whole training set. On the one hand, the correspondence of landmarks is related the quality of shape model. On the other hand, in clinical application some key positions of landmarks should be specified by physicians referring to the anatomic structure. In this paper, we develop an interactive method to build SSM that the landmark distribution can be modified manually without influencing the model quality. We extend an existing remeshing method to produce a model prototype in advance and surface features driven registration to insure the universal optimization of correspondence. The key landmarks are fixed during the prototype generation. We experimented and evaluated the proposed SSM method for lung regions, the deformations of which are considerable large.

DOI： 10.1016/j.optlastec.2017.09.018

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85030029527&origin=inward

　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