Prof. Shoji Kido Profile

Prof. Shoji Kido

at Osaka Univ

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Author

Publications (15)

Proceedings Article | 7 April 2023 Presentation + Paper

Bi-modal network combining convolutional neural network and TabNet, differentiating spinal tumors based on images and clinical risk factors

Kosuke Kita, Takahito Fujimori, Yuki Suzuki, Seiji Okada, Shoji Kido

Proceedings Volume 12465, 1246506 (2023) https://doi.org/10.1117/12.2646940

KEYWORDS: Tumors, Data modeling, Magnetic resonance imaging, Neural networks, Performance modeling, Convolutional neural networks, Deep learning, Education and training, Image classification, Diagnostics

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Proceedings Article | 7 April 2023 Poster + Paper

Automatic characterization of liver tumors from multi-phase CT images

Keita Otani, Daiki Nishigaki, Taro Hatsutani, Takeshi Takamoto, Yuki Suzuki, Shoji Kido, Noriyuki Tomiyama

Proceedings Volume 12465, 124651Q (2023) https://doi.org/10.1117/12.2648644

KEYWORDS: Tumors, Liver, Image classification, Computed tomography, Classification systems, Radiology, Feature extraction, Liver cancer, Performance modeling, Image analysis

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Proceedings Article | 20 April 2021 Paper

Lung region extraction from CT images of diffuse lung disease cases by deep learning

Daisuke Kurata, Shoji Kido, Yasushi Hirano

Proceedings Volume 11792, 1179206 (2021) https://doi.org/10.1117/12.2590660

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Proceedings Article | 20 April 2021 Paper

Classification of lung cancer into adenocarcinoma and squamous cell carcinoma, and visualization of the grounds of classification

Erena Ise, Shoji Kido, Yasushi Hirano

Proceedings Volume 11792, 117920B (2021) https://doi.org/10.1117/12.2590695

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Proceedings Article | 16 March 2020 Paper

Weak supervision in convolutional neural network for semantic segmentation of diffuse lung diseases using partially annotated dataset

Yuki Suzuki, Kazuki Yamagata, Masahiro Yanagawa, Shoji Kido, Noriyuki Tomiyama

Proceedings Volume 11314, 113142A (2020) https://doi.org/10.1117/12.2548930

KEYWORDS: Image segmentation, Lung, Convolutional neural networks, Machine learning, Computed tomography, Computer aided diagnosis and therapy

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Proceedings Article | 27 March 2019 Paper

Segmentation of lung region from chest x-ray images using U-net

Keigo Furutani, Yasushi Hirano, Shoji Kido

Proceedings Volume 11050, 1105010 (2019) https://doi.org/10.1117/12.2521594

KEYWORDS: Lung, Image segmentation, Chest imaging, Data modeling, Heart, Computer aided diagnosis and therapy, Medical imaging, Cancer, Target detection, Computer programming

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Proceedings Article | 27 March 2019 Paper

Analysis of the effects of transfer learning on opacity classification of diffuse lung diseases using convolutional neural network

Ami Atsumo, Shingo Mabu, Shoji Kido, Yasushi Hirano, Takashi Kuremoto

Proceedings Volume 11050, 1105017 (2019) https://doi.org/10.1117/12.2521229

KEYWORDS: Opacity, Lung, Convolutional neural networks, Computer aided diagnosis and therapy, Analytical research, Medical imaging, Machine learning, Data modeling, Image classification, Scientific research

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Proceedings Article | 27 March 2019 Paper

Unsupervised and semi-supervised learning for efficient opacity annotation of diffuse lung diseases

Shingo Mabu, Shoji Kido, Yasushi Hirano, Takashi Kuremoto

Proceedings Volume 11050, 110501D (2019) https://doi.org/10.1117/12.2519929

KEYWORDS: Opacity, Machine learning, Lung, Feature extraction, Computer aided diagnosis and therapy, Computed tomography, Image classification, Emphysema, Medical imaging, Diagnostics

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Proceedings Article | 27 March 2019 Paper

Detection of pulmonary nodules on chest x-ray images using R-CNN

R. Takemiya, S. Kido, Y. Hirano, S. Mabu

Proceedings Volume 11050, 110500W (2019) https://doi.org/10.1117/12.2521652

KEYWORDS: Chest imaging, Opacity, Computer aided diagnosis and therapy, Convolutional neural networks, Neural networks, Lung cancer, Standards development, Databases, X-ray technology, X-ray imaging

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Proceedings Article | 28 February 2018 Paper

Deep radiomic prediction with clinical predictors of the survival in patients with rheumatoid arthritis-associated interstitial lung diseases

Radin Nasirudina, Janne Näppi, Chinatsu Watari, Mikio Matsuhiro, Toru Hironaka, Shoji Kido, Hiroyuki Yoshida

Proceedings Volume 10575, 105753R (2018) https://doi.org/10.1117/12.2293374

KEYWORDS: Lung, Computed tomography, Feature extraction, Performance modeling, Pulmonary function tests, Convolutional neural networks

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We developed and evaluated the effect of our deep-learning-derived radiomic features, called deep radiomic features (DRFs), together with their combination with clinical predictors, on the prediction of the overall survival of patients with rheumatoid arthritis-associated interstitial lung disease (RA-ILD). We retrospectively identified 70 RA-ILD patients with thin-section lung CT and pulmonary function tests. An experienced observer delineated regions of interest (ROIs) from the lung regions on the CT images, and labeled them into one of four ILD patterns (ground-class opacity, reticulation, consolidation, and honeycombing) or a normal pattern. Small image patches centered at individual pixels on these ROIs were extracted and labeled with the class of the ROI to which the patch belonged. A deep convolutional neural network (DCNN), which consists of a series of convolutional layers for feature extraction and a series of fully connected layers, was trained and validated with 5-fold cross-validation for classifying the image patches into one of the above five patterns. A DRF vector for each patch was identified as the output of the last convolutional layer of the DCNN. Statistical moments of each element of the DRF vectors were computed to derive a DRF vector that characterizes the patient. The DRF vector was subjected to a Cox proportional hazards model with elastic-net penalty for predicting the survival of the patient. Evaluation was performed by use of bootstrapping with 2,000 replications, where concordance index (C-index) was used as a comparative performance metric. Preliminary results on clinical predictors, DRFs, and their combinations thereof showed (a) Gender and Age: C-index 64.8% [95% confidence interval (CI): 51.7, 77.9]; (b) gender, age, and physiology (GAP index): C-index: 78.5% [CI: 70.50 86.51], P < 0.0001 in comparison with (a); (c) DRFs: C-index 85.5% [CI: 73.4, 99.6], P < 0.0001 in comparison with (b); and (d) DRF and GAP: C-index 91.0% [CI: 84.6, 97.2], P < 0.0001 in comparison with (c). Kaplan-Meier survival curves of patients stratified to low- and high-risk groups based on the DRFs showed a statistically significant (P < 0.0001) difference. The DRFs outperform the clinical predictors in predicting patient survival, and a combination of the DRFs and GAP index outperforms either one of these predictors. Our results indicate that the DRFs and their combination with clinical predictors provide an accurate prognostic biomarker for patients with RA-ILD.

Proceedings Article | 27 February 2018 Paper

Deep neural network convolution (NNC) for three-class classification of diffuse lung disease opacities in high-resolution CT (HRCT): consolidation, ground-glass opacity (GGO), and normal opacity

Noriaki Hashimoto, Kenji Suzuki, Junchi Liu, Yasushi Hirano, Heber MacMahon, Shoji Kido

Proceedings Volume 10575, 1057536 (2018) https://doi.org/10.1117/12.2293550

KEYWORDS: Opacity, Lung, Neural networks, Convolution, Computed tomography, Classification systems, Image classification, Image segmentation, Computer aided diagnosis and therapy, Algorithm development

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Proceedings Article | 27 February 2018 Paper

Opacity annotation of diffuse lung diseases using deep convolutional neural network with multi-channel information

Shingo Mabu, Shoji Kido, Noriaki Hashimoto, Yasushi Hirano, Takashi Kuremoto

Proceedings Volume 10575, 1057534 (2018) https://doi.org/10.1117/12.2293422

KEYWORDS: Opacity, Lung, Computed tomography, Emphysema, Convolutional neural networks, Image classification, Image filtering, Computer aided diagnosis and therapy, Feature extraction

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Proceedings Article | 28 February 2009 Paper

Classification of patterns for diffuse lung diseases in thoracic CT images by AdaBoost algorithm

Masayuki Kuwahara, Shoji Kido, Hayaru Shouno

Proceedings Volume 7260, 726037 (2009) https://doi.org/10.1117/12.811497

KEYWORDS: Computed tomography, Lung, Image classification, Image filtering, Classification systems, Computer aided diagnosis and therapy, Feature extraction, Image segmentation, CAD systems, Opacity

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Proceedings Article | 28 February 2009 Paper

Automated extraction of pleural effusion in three-dimensional thoracic CT images

Shoji Kido, Akinori Tsunomori

Proceedings Volume 7260, 72600D (2009) https://doi.org/10.1117/12.810559

KEYWORDS: Lung, Computed tomography, Image segmentation, Image registration, Bone, Liver, Image processing, Natural surfaces, Feature extraction, 3D image processing

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Proceedings Article | 10 March 2006 Paper

Automatic segmentation of pulmonary nodules on CT images by use of NCI lung image database consortium

Rie Tachibana, Shoji Kido

Proceedings Volume 6144, 61440M (2006) https://doi.org/10.1117/12.653366

KEYWORDS: Image segmentation, Chest, Chromium, Image processing algorithms and systems, Lung, Databases, Computed tomography, X-ray computed tomography, Solids, Image processing

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Showing 5 of 15 publications

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