研究者詳細 - 神谷　亨

論文 - 神谷　亨

分割表示751 件中 41 - 60 件目／全件表示 >>

Noise-robust Grasp Position Estimation Using PointNet 査読有り国際誌

Ryoto Fujikame, Yuchao Zheng, Shin Ishiyama, Huimin Lu, Kamiya Tohru, Yoshihisa Nakatoh, Seiichi Serikawa

The 8th International Symposium on Artificial Intelligence and Robotics 2023年10月

Extraction of Lung Tumor Regions from Thoracic CT Images Using an Improved U-Net 査読有り国際誌

Takashi, Kamiya, Terasawa, Aoki

Proc. of the 23nd International Conference on Control, Automation and Systems 1489 - 1493 2023年10月

An Image Registration Technique for Brain MR Images Using Linear Transform by 3DCNN 査読有り国際誌

Baba, Kamiya

Proc. of the 23nd International Conference on Control, Automation and Systems 1762 - 1765 2023年10月

Recognition of Specific Parts of Plastic Bottles Using Improved DeepLab v3+ 査読有り国際誌

Ideta, Kamiya

Proc. of the 23nd International Conference on Control, Automation and Systems 1758 - 1761 2023年10月

Classification of histological types of primary lung cancer from CT images using clinical information 査読有り国際誌

Honda, Kamiya, Kido

Proc. of the 23nd International Conference on Control, Automation and Systems 1753 - 1757 2023年10月

Identification of Root Resorption on Panoramic X-ray Image Based on EfficientNet 査読有り国際誌

Sakata, Kamiya, Oda, Morimoto

Proc. of the 23nd International Conference on Control, Automation and Systems 1749 - 1752 2023年10月

Remote Sensing Image Registration Based on Improved Geometric-matching CNN 査読有り国際誌

Morishima, Lu, Kamiya

Proc. of the 23nd International Conference on Control, Automation and Systems 1745 - 1748 2023年10月

A Detection Method for Nodular Shadows from Temporal Subtraction Images Using A Machine Learning Technique Incorporated Radiomics Features 査読有り国際誌

Baba, Kamiya, Terasawa, Aoki, Kido

Proc. of the 23nd International Conference on Control, Automation and Systems 1741 - 1744 2023年10月

An Denoising Method Low-Dose CT Image Using Image Restoration Convolutional Neural Network 査読有り国際誌

Sadamatsu, Murakami, Li, Kamiya

Proc. of the 23nd International Conference on Control, Automation and Systems 1737 - 1740 2023年10月

Classification of Driver Gene Mutations from 3D-CT Images Based on Radiomics Features 査読有り国際誌

Watanabe, Kamiya, Terasawa, Aoki

Proc. of the 23nd International Conference on Control, Automation and Systems 1733 - 1736 2023年10月

Identification of Nodular Shadows from CT Images Using Improved CoAtNet Incorporated Clinical Recording 査読有り国際誌

Nishitaki, Kamiya, Kido

Proc. of the 23nd International Conference on Control, Automation and Systems 1727 - 1732 2023年10月

Facial Symmetry Analysis for Cleft Lip Patients from 4D Point Cloud Data 査読有り国際誌

Kihara, Kimura-Nomoto, Okawachi, Li, Nakamura, Kamiya

Proc. of the 23nd International Conference on Control, Automation and Systems 1502 - 1505 2023年10月

Automatic Segmentation of Finger Bone Regions from CR Image Using the Improved HRNet 査読有り国際誌

Hiraoka, Kamiya, Aoki

Proc. of the 23nd International Conference on Control, Automation and Systems 1498 - 1501 2023年10月

A Method for Denoising from Low-Dose CT images Based on Noise2Noise 査読有り国際誌

Sawada, Murakami, Li, Kamiya

Proc. of the 23nd International Conference on Control, Automation and Systems 1494 - 1497 2023年10月

FPA LRSE U-Netを用いたCT画像からの脊椎領域の自動抽出

木佐貫　航平，神谷　亨，寺澤　岳，青木

第42回日本医用画像工学会大会予稿集 72 - 73 2023年09月

AI技術とコンピュータ支援診断への応用招待有り査読有り

神谷

画像ラボ 3 ( 8 ) 29 - 34 2023年08月

3次元点群動画像を用いた顔の時系列変化の検出および非対称度評価

木原　成海，野元　菜美子，大河内　孝子，李　光旭, 中村　典史, 神谷:

第42回日本医用画像工学会大会予稿集 217 - 218 2023年07月

3DCNNを用いた脳MR画像の3次元位置合わせ

馬場　星太郎，神谷

第42回日本医用画像工学会大会予稿集 227 - 228 2023年07月

非侵襲的なドライバー遺伝子変異有無の識別のためのRadiomics特徴量に基づく2クラス分類

渡邊，神谷，寺澤，青木

第42回日本医用画像工学会大会予稿集 219 - 220 2023年07月

Res BCDU-SCB Net: A Lung CT Image Segmentation based on SC-BLSTM 査読有り国際誌

Wang T., Li G（天津工大）., Tang C（天津工大）., Kamiya T.

ACM International Conference Proceeding Series 42 - 46 2023年07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）

Due to the unique physiological structure of human lung tissue, segmentation of the lung parenchymal regions is a very challenging task. Traditional image segmentation methods have certain limitations on practical applications. Therefore, lung tissue segmentation methods based on deep learning have become a research hotspot in recent years. Res BCDU-Net may overcome the problem of high false positives in other semi-automatic segmentation, but it still has the problem of low accuracy in edge segmentation, which is probably caused by the unclear input of a single CT image. Therefore, we propose to utilize three consecutive CT images as the input, which employs inter-slice context to restrain the false positive. When a single input CT image is not clear, the accuracy of lung segmentation results can be improved by referring to its previous and next slices. Based on the input of three continuous images, the spatial information in the process of lung segmentation is added in this paper. We build a Res BCDU-SCB network and collect and integrate spatial information through SC-BLSTM module. An SC-attention module is designed to take both finer-grained spatial information and rich semantic information into account. In this paper, we append BLSTM (Bidirectional Convolutional Long Short-term Memory) module with SC-attention module, named SC-BLSTM, to decrease performance degradation caused by ambiguous boundaries and variable lung parenchymal shapes. The results of extensive experiments on lung CT images (LIDC-IDRI database) show that it may improve the final segmentation accuracy and reduce the false positives, which confirms the effectiveness of our proposed method.

DOI： 10.1145/3613307.3613316

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85175976936&origin=inward

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