Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： https://doi.org/10.1109/CVPR42600.2020.00388

Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/CVPR.2019.01288

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1109/TMI.2015.2391095

Language：English   Publisher：Medical Image Analysis  

Automatic image-based severity estimation is an important task in computer-aided diagnosis. Severity estimation by deep learning requires a large amount of training data to achieve a high performance. In general, severity estimation uses training data annotated with discrete (i.e., quantized) severity labels. Annotating discrete labels is often difficult in images with ambiguous severity, and the annotation cost is high. In contrast, relative annotation, in which the severity between a pair of images is compared, can avoid quantizing severity and thus makes it easier. We can estimate relative disease severity using a learning-to-rank framework with relative annotations, but relative annotation has the problem of the enormous number of pairs that can be annotated. Therefore, the selection of appropriate pairs is essential for relative annotation. In this paper, we propose a deep Bayesian active learning-to-rank that automatically selects appropriate pairs for relative annotation. Our method preferentially annotates unlabeled pairs with high learning efficiency from the model uncertainty of the samples. We prove the theoretical basis for adapting Bayesian neural networks to pairwise learning-to-rank and demonstrate the efficiency of our method through experiments on endoscopic images of ulcerative colitis on both private and public datasets. We also show that our method achieves a high performance under conditions of significant class imbalance because it automatically selects samples from the minority classes.

DOI： 10.1016/j.media.2024.103262

Scopus

PubMed

Publisher：Image and Vision Computing  

Medical image segmentation is an important task in medical imaging and diagnosis. Data augmentation can substantially improve the accuracy of medical image segmentation when the dataset has a small amount of medical images. However, the data augmentation methods for medical image are usually based on big models that require extensive search space. Furthermore, excessively complex models often have a heavy burden for the general healthcare organization or researcher. To address this problem, we propose a method of data augmentation that is simple to implement even for the general researcher and simple to transplant across various models. Here we introduce our new methods called KeepMask and KeepMix, which can be simply ported to a variety of models and provide high performance. These methods allow data augmentation without any effect on the target organ or lesion and can also be adapted to multi-class segmentation. KeepMask and KeepMix can not only perturb the background of an existing medical image but also add target organs that are not present to it and generate new images based on the image. In this paper, we performed our methods on both binary class datasets and multi-class datasets and obtained better performance. We conducted numerous experiments showing the predicted segmentation images using our proposed methods obtained more accurate boundaries.

DOI： 10.1016/j.imavis.2024.105056

Web of Science

Scopus

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1016/j.knee.2024.03.008

Web of Science

Scopus

PubMed

Language：English   Publisher：Modern Pathology  

Several studies have developed various artificial intelligence (AI) models for immunohistochemical analysis of programmed death ligand 1 (PD-L1) in patients with non–small cell lung carcinoma; however, none have focused on specific ways by which AI-assisted systems could help pathologists determine the tumor proportion score (TPS). In this study, we developed an AI model to calculate the TPS of the PD-L1 22C3 assay and evaluated whether and how this AI-assisted system could help pathologists determine the TPS and analyze how AI-assisted systems could affect pathologists’ assessment accuracy. We assessed the 4 methods of the AI-assisted system: (1 and 2) pathologists first assessed and then referred to automated AI scoring results (1, positive tumor cell percentage; 2, positive tumor cell percentage and visualized overlay image) for final confirmation, and (3 and 4) pathologists referred to the automated AI scoring results (3, positive tumor cell percentage; 4, positive tumor cell percentage and visualized overlay image) while determining TPS. Mixed-model analysis was used to calculate the odds ratios (ORs) with 95% CI for AI-assisted TPS methods 1 to 4 compared with pathologists’ scoring. For all 584 samples of the tissue microarray, the OR for AI-assisted TPS methods 1 to 4 was 0.94 to 1.07 and not statistically significant. Of them, we found 332 discordant cases, on which the pathologists’ judgments were inconsistent; the ORs for AI-assisted TPS methods 1, 2, 3, and 4 were 1.28 (1.06-1.54; P = .012), 1.29 (1.06-1.55; P = .010), 1.28 (1.06-1.54; P = .012), and 1.29 (1.06-1.55; P = .010), respectively, which were statistically significant. For discordant cases, the OR for each AI-assisted TPS method compared with the others was 0.99 to 1.01 and not statistically significant. This study emphasized the usefulness of the AI-assisted system for cases in which pathologists had difficulty determining the PD-L1 TPS.

DOI： 10.1016/j.modpat.2024.100485

Web of Science

Scopus

PubMed

Language：English   Publisher：Nature Communications  

Cell states are regulated by the response of signaling pathways to receptor ligand-binding and intercellular interactions. High-resolution imaging has been attempted to explore the dynamics of these processes and, recently, multiplexed imaging has profiled cell states by achieving a comprehensive acquisition of spatial protein information from cells. However, the specificity of antibodies is still compromised when visualizing activated signals. Here, we develop Precise Emission Canceling Antibodies (PECAbs) that have cleavable fluorescent labeling. PECAbs enable high-specificity sequential imaging using hundreds of antibodies, allowing for reconstruction of the spatiotemporal dynamics of signaling pathways. Additionally, combining this approach with seq-smFISH can effectively classify cells and identify their signal activation states in human tissue. Overall, the PECAb system can serve as a comprehensive platform for analyzing complex cell processes.

DOI： 10.1038/s41467-024-47989-9

Web of Science

Scopus

PubMed

Language：English   Publishing type：Research paper (international conference proceedings)  

Language：English   Publisher：John Wiley & Sons Australia, Ltd  

Language：English   Publishing type：Research paper (scientific journal)  

Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/ICASSP48485.2024.10448425

Scopus

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1038/s41698-024-00515-y

Web of Science

Scopus

PubMed

Publisher：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)  

The PD-L1 rate, the number of PD-L1 positive tumor cells over the total number of all tumor cells, is an important metric for immunotherapy. This metric is recorded as diagnostic information with pathological images. In this paper, we propose a proportion estimation method with a small amount of cell-level annotation and proportion annotation, which can be easily collected. Since the PD-L1 rate is calculated from only ‘tumor cells’ and not using ‘non-tumor cells’, we first detect tumor cells with a detection model. Then, we estimate the PD-L1 proportion by introducing a masking technique to ‘learning from label proportion’. In addition, we propose a weighted focal proportion loss to address data imbalance problems. Experiments using clinical data demonstrate the effectiveness of our method. Our method achieved the best performance in comparisons.

DOI： 10.1007/978-3-031-58171-7_12

Web of Science

Scopus

Publisher：Proceedings - International Symposium on Biomedical Imaging  

This paper brings attention to a new issue in the development of datasets for AI-based pathological image analysis: the domain shift problem stemming from the storage period until digital scanning. Pathological slides that were sliced and stained in the past may also be scanned to prepare a dataset for AI in addition to the newly stained tissue. However, it leads to changes in their appearance and causes domain shift. We propose a domain generalization method that leverages the storage period as sub-domains. Furthermore, we introduce the ordinal adversarial loss that can perform well for ordinal domain classes. The experiments show the effectiveness of using the storage period as a sub-domain and the ordinal adversarial loss.

DOI： 10.1109/ISBI56570.2024.10635214

Scopus

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1016/j.modpat.2023.100302

Web of Science

Scopus

PubMed

Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/ICCV51070.2023.01519

Web of Science

Scopus

Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1007/978-3-031-43993-3_47

Web of Science

Scopus

Language：English   Publishing type：Research paper (international conference proceedings)  

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1111/den.14677

Web of Science

Scopus

PubMed

Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/EMBC40787.2023.10340619

Web of Science

Scopus

PubMed

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1111/exd.14840

Web of Science

Scopus

PubMed

Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/ICASSP49357.2023.10097069

Scopus

Language：English   Publishing type：Research paper (international conference proceedings)  

Language：English   Publishing type：Research paper (international conference proceedings)  

Language：English   Publishing type：Research paper (international conference proceedings)  

Language：English   Publishing type：Research paper (international conference proceedings)  

Web of Science

Web of Science

Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/WACV56688.2023.00171

Web of Science

Scopus

Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/WACV56688.2023.00320

Web of Science

Scopus

Publisher：Proceedings - International Symposium on Biomedical Imaging  

This paper proposes a semi-supervised pathological image segmentation method that can improve the segmentation using a large amount of unlabeled data. Typical pseudo labeling methods select pseudo labels from unlabeled data to be used for re-training based on the confidence of each patch. However, the initial estimation is not accurate, so the pseudo labels contain many inaccurate labels. This may affect the segmentation performance. The highlight of this study is that we avoid selecting incorrect pseudo labels by introducing a spatial distribution model in a whole slide image. This is based on the assumption that a tumor region forms a cluster in tissue. This improves pseudo label selection and segmentation performance. Experimental results demonstrate the effectiveness of our method, where our method achieved the best segmentation performance in comparison.

DOI： 10.1109/ISBI53787.2023.10230407

Web of Science

Scopus

CiNii Research

Publisher：Proceedings - International Symposium on Biomedical Imaging  

The development of medical image segmentation using deep learning can significantly support doctors' diagnoses. Deep learning needs large amounts of data for training, which also requires data augmentation to extend diversity for preventing overfitting. However, the existing methods for data augmentation of medical image segmentation are mainly based on models which need to update parameters and cost extra computing resources. We proposed data augmentation methods designed to train a high-accuracy deep learning network for medical image segmentation. The proposed data augmentation approaches are called KeepMask and KeepMix, which can create medical images by better identifying the boundary of the organ with no more parameters. Our methods achieved better performance and obtained more precise boundaries for medical image segmentation on datasets. The dice coefficient of our methods achieved 94.15% (3.04% higher than baseline) on CHAOS and 74.70% (5.25% higher than baseline) on MSD spleen with Unet.

DOI： 10.1109/ISBI53787.2023.10230495

Web of Science

Scopus

CiNii Research

CiNii Research

Publisher：Proceedings - International Symposium on Biomedical Imaging  

Semi-supervised domain adaptation is a technique to build a classifier for a target domain by modifying a classifier in another (source) domain using many unlabeled samples and a small number of labeled samples from the target domain. In this paper, we develop a semi-supervised domain adaptation method, which has robustness to class-imbalanced situations, which are common in medical image classification tasks. For robustness, we propose a weakly-supervised clustering pipeline to obtain high-purity clusters and utilize the clusters in representation learning for domain adaptation. The proposed method showed state-of-the-art performance in the experiment using severely class-imbalanced pathological image patches.

DOI： 10.1109/ISBI53787.2023.10230451

Web of Science

Scopus

CiNii Research

Publisher：Proceedings - International Symposium on Biomedical Imaging  

The domain shift in pathological segmentation is an important problem, where a network trained by a source domain (collected at a specific hospital) does not work well in the target domain (from different hospitals) due to the different image features. Due to the problems of class imbalance and different class prior of pathology, typical unsupervised domain adaptation methods do not work well by aligning the distribution of source domain and target domain. In this paper, we propose a cluster entropy for selecting an effective whole slide image (WSI) that is used for semi-supervised domain adaptation. This approach can measure how the image features of the WSI cover the entire distribution of the target domain by calculating the entropy of each cluster and can significantly improve the performance of domain adaptation. Our approach achieved competitive results against the prior arts on datasets collected from two hospitals.

DOI： 10.1109/ISBI53787.2023.10230359

Web of Science

Scopus

CiNii Research

CiNii Research

Language：English   Publisher：Arthritis Research and Therapy  

Background: X-ray images are commonly used to assess the bone destruction of rheumatoid arthritis. The purpose of this study is to propose an automatic-bone-destruction-evaluation system fully utilizing deep neural networks (DNN). This system detects all target joints of the modified Sharp/van der Heijde score (SHS) from a hand X-ray image. It then classifies every target joint as intact (SHS = 0) or non-intact (SHS ≥ 1). Methods: We used 226 hand X-ray images of 40 rheumatoid arthritis patients. As for detection, we used a DNN model called DeepLabCut. As for classification, we built four classification models that classify the detected joint as intact or non-intact. The first model classifies each joint independently, whereas the second model does it while comparing the same contralateral joint. The third model compares the same joint group (e.g., the proximal interphalangeal joints) of one hand and the fourth model compares the same joint group of both hands. We evaluated DeepLabCut’s detection performance and classification models’ performances. The classification models’ performances were compared to three orthopedic surgeons. Results: Detection rates for all the target joints were 98.0% and 97.3% for erosion and joint space narrowing (JSN). Among the four classification models, the model that compares the same contralateral joint showed the best F-measure (0.70, 0.81) and area under the curve of the precision-recall curve (PR-AUC) (0.73, 0.85) regarding erosion and JSN. As for erosion, the F-measure and PR-AUC of this model were better than the best of the orthopedic surgeons. Conclusions: The proposed system was useful. All the target joints were detected with high accuracy. The classification model that compared the same contralateral joint showed better performance than the orthopedic surgeons regarding erosion.

DOI： 10.1186/s13075-022-02914-7

Web of Science

Scopus

PubMed

Language：English   Publishing type：Research paper (scientific journal)  

DOI： https://doi.org/10.1186/s13075-022-02914-7

Language：English   Publishing type：Research paper (international conference proceedings)  

Language：English   Publishing type：Research paper (international conference proceedings)  

Language：English   Publishing type：Research paper (international conference proceedings)  

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1109/ACCESS.2022.3155769

Web of Science

Scopus

Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/WACV51458.2022.00182

Web of Science

Scopus

Language：Japanese   Publisher：Japan Society of Civil Engineers  

<p>There has been a growing demand to strengthen existing disaster prevention education tobe prepared for the huge tsunami expected to occur in the near future. Virtual reality, which allows people to virtually experience natural disasters, has a strong potential in fostering disaster awareness among citizens. However, it requires enormous human and time resources to map the texture of structures to urban area-imitating virtual space. On the other hand, pix2pixHD proposed by Wang et al. can generate high-resolution synthetic images by learning from reference images, label data, and object boundary data. In this study, we applied pix2pixHD and transfer learning, which diverts networks trained on other similar domains, to verify texture mapping of urban areas in Japan from a limited set of image data.</p>

DOI： 10.11532/jsceiii.3.j2_551

CiNii Research

Language：Japanese   Publisher：MEDICAL IMAGING AND INFORMATION SCIENCES  

<p>Phoacouastic (PA) imaging has been gaining attention as a new imaging modality that can non-invasively visualize blood vessels inside biological tissues. In this paper, the reconstruction process of PA, and its technical issues. In the process of imaging large body parts through multi-scan fusion, alignment turns out to be an important issue, since body motion degrades image quality. In this paper, we carefully examine the characteristics of PA images and propose a novel registration method that achieves better alignment while effectively decomposing the shot volumes into low-rank foreground (blood vessels), dense background (noise), and sparse complement (corruption) components on the basis of the PA characteristics. The results of experiments using a challenging real dataset demonstrate the efficacy of the proposed method, which significantly improved image quality, and had the best alignment accuracy among the state-of-the-art methods tested. We also introduce several clinical application of photoacoustic imaging, such as planning of flap surgery.</p>

DOI： 10.11318/mii.39.14

CiNii Research

Publisher：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)  

We propose a deep low-rank alignment network that can simultaneously perform non-rigid alignment and noise decomposition for multiple images despite severe noise and sparse corruptions. To address this challenging task, we introduce a low-rank loss in deep learning under the assumption that a set of well-aligned, well-denoised images should be linearly correlated, and thus, that a matrix consisting of the images should be low-rank. This allows us to remove the noise and corruption from input images in a self-supervised learning manner (i.e., without requiring supervised data). In addition, we introduce multi-input attention modules into Siamese U-nets in order to aggregate the corruption information from the set of images. To the best of our knowledge, this is the first attempt to introduce a low-rank loss for deep learning-based non-rigid alignment. Experiments using both synthetic data and real medical image data demonstrate the effectiveness of the proposed method. The code will be publicly available in https://github.com/asanomitakanori/Unsupervised-Deep-Non-Rigid-Alignment-by-Low-Rank-Loss-and-Multi-Input-Attention.

DOI： 10.1007/978-3-031-16446-0_18

Web of Science

Scopus

Publisher：IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops  

Multi-class cell detection (cancer or non-cancer) from a whole slide image (WSI) is an important task for pathological diagnosis. Cancer and non-cancer cells often have a similar appearance, so it is difficult even for experts to classify a cell from a patch image of individual cells. They usually identify the cell type not only on the basis of the appearance of a single cell but also on the context of the surrounding cells. For using such information, we propose a multi-class cell-detection method that introduces a modified self-attention to aggregate the surrounding image features of both classes. Experimental results demonstrate the effectiveness of the proposed method; our method achieved the best performance compared with a method, which simply uses the standard self-attention method.

DOI： 10.1109/CVPRW56347.2022.00202

Web of Science

Scopus

Publisher：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)  

Automatic image-based disease severity estimation generally uses discrete (i.e., quantized) severity labels. Annotating discrete labels is often difficult due to the images with ambiguous severity. An easier alternative is to use relative annotation, which compares the severity level between image pairs. By using a learning-to-rank framework with relative annotation, we can train a neural network that estimates rank scores that are relative to severity levels. However, the relative annotation for all possible pairs is prohibitive, and therefore, appropriate sample pair selection is mandatory. This paper proposes a deep Bayesian active-learning-to-rank, which trains a Bayesian convolutional neural network while automatically selecting appropriate pairs for relative annotation. We confirmed the efficiency of the proposed method through experiments on endoscopic images of ulcerative colitis. In addition, we confirmed that our method is useful even with the severe class imbalance because of its ability to select samples from minor classes automatically.

DOI： 10.1007/978-3-031-12053-4_45

Web of Science

Scopus

Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/EMBC46164.2021.9630809

Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/EMBC46164.2021.9630720

Language：English   Publishing type：Research paper (international conference proceedings)  

Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： https://doi.org/10.1007/978-3-030-87196-3_44

Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1007/978-3-030-87237-3_36

Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1007/978-3-030-87237-3_41

Language：English   Publishing type：Research paper (international conference proceedings)  

Language：English   Publishing type：Research paper (scientific journal)  

Language：English   Publishing type：Research paper (international conference proceedings)  

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1038/s41598-019-47213-5

Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1007/978-3-030-32239-7_44

Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1007/978-3-030-32254-0_47

Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1007/978-3-030-32239-7_72

Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/EMBC.2019.8857011

Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/EMBC.2019.8856465

Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/ISBI.2019.8759249

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1177/1753193418807833

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1109/TPAMI.2019.2903496

Scopus

Other Link： https://ieeexplore.ieee.org/document/8661549

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1038/sdata.2018.237

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1016/j.jdermsci.2018.07.006

Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1007/978-3-319-66185-8_2

Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/ICCVW.2017.20

Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1007/978-3-319-66182-7_80

Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1007/978-3-319-46726-9_38

Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/CVPRW.2016.168

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1016/j.mvr.2015.12.005

Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.2316/P.2013.791-057

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1016/j.jbiomech.2012.01.032

Language：English   Publishing type：Research paper (scientific journal)  

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1371/journal.pone.0027672

Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/ISBI.2011.5872571

Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/ISBI.2011.5872832

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1109/TMI.2010.2089384

Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/WACV.2011.5711528

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1109/TMI.2010.2089384

Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/ISBI.2010.5490394

Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/ISBI.2010.5490399

Language：English   Publishing type：Research paper (international conference proceedings)  

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1109/TCSI.2003.819827

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1109/TCSI.2003.819827

Language：English   Publishing type：Research paper (international conference proceedings)  

Event date： 2024.3

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：沖縄青年会館，那覇市   Country：Japan  

Event date： 2024.3

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：広島大学，東広島市   Country：Japan  

Event date： 2024.3

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：沖縄青年会館，那覇市   Country：Japan  

Event date： 2024.3

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：沖縄青年会館，那覇市   Country：Japan  

Event date： 2023.12

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：富山国際会議場，富山市   Country：Japan  

Event date： 2023.12

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：富山国際会議場，富山市   Country：Japan  

Event date： 2023.12 - 2022.12

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：富山国際会議場，富山市   Country：Japan  

Event date： 2023.11

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：鳥取県立生涯学習センター, 鳥取市   Country：Japan  

Event date： 2023.11

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：鳥取県立生涯学習センター, 鳥取市   Country：Japan  

Event date： 2023.11

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：久留米シティプラザ，久留米市   Country：Japan  

Event date： 2023.10

Language：English   Presentation type：Oral presentation (general)  

Event date： 2023.9

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2023.9

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2023.9

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2023.9

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2023.7

Language：Japanese  

Venue：浜松   Country：Japan  

Event date： 2023.7

Language：Japanese  

Venue：浜松   Country：Japan  

Event date： 2023.7

Language：Japanese  

Venue：浜松   Country：Japan  

Event date： 2023.3

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：沖縄青年会館，那覇市   Country：Japan  

Event date： 2023.3

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：沖縄青年会館，那覇市   Country：Japan  

Event date： 2023.3

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：はこだて未来大学，函館市   Country：Japan  

Event date： 2023.3

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：はこだて未来大学，函館市   Country：Japan  

Event date： 2023.1

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2022.7

Language：Japanese  

Country：Japan  

Event date： 2022.7

Language：Japanese  

Country：Japan  

Event date： 2022.7

Language：Japanese  

Venue：姫路   Country：Japan  

Event date： 2022.7

Language：Japanese  

Venue：姫路   Country：Japan  

Event date： 2022.7

Language：Japanese  

Venue：姫路   Country：Japan  

Event date： 2022.5

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：豊田工業大学   Country：Japan  

Event date： 2022.2

Language：Japanese   Presentation type：Oral presentation (invited, special)  

Venue：online   Country：Japan  

Event date： 2022.1

Language：Japanese   Presentation type：Oral presentation (invited, special)  

Venue：online   Country：Japan  

Event date： 2021.12

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：online   Country：Japan  

Event date： 2021.12

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：online   Country：Japan  

Event date： 2021.9

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：online   Country：Japan  

Event date： 2021.9

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：online   Country：Japan  

Event date： 2021.8

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2021.7

Language：Japanese  

Country：Japan  

Event date： 2021.7

Language：Japanese  

Country：Japan  

Event date： 2021.7

Language：Japanese  

Country：Japan  

Event date： 2021.7

Language：Japanese  

Country：Japan  

Event date： 2020.10

Language：English   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2020.10

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2020.10

Language：Japanese  

Country：Japan  

Event date： 2020.10

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：online   Country：Japan  

Event date： 2020.10

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2020.9

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：online   Country：Japan  

Event date： 2020.9

Language：English   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2020.9

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：online   Country：Japan  

Event date： 2020.8

Language：Japanese  

Venue：online   Country：Japan  

Event date： 2020.8

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：online   Country：Japan  

Event date： 2020.8

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2020.8

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2020.7

Language：Japanese  

Country：Japan  

Event date： 2020.7

Language：Japanese   Presentation type：Oral presentation (invited, special)  

Venue：online   Country：Japan  

Event date： 2020.5

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：online   Country：Japan  

Event date： 2020.5

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：online   Country：Japan  

Event date： 2020.3

Language：English   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2020.3

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2020.2

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：静岡   Country：Japan  

Event date： 2020.1

Language：Japanese  

Venue：東京   Country：Japan  

Event date： 2020.1

Language：Japanese  

Venue：東京   Country：Japan  

Event date： 2019.12

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：大分大学、大分   Country：Japan  

Event date： 2019.11

Language：English  

Country：Japan  

Event date： 2019.11

Language：English  

Country：Japan  

Event date： 2019.11

Language：English  

Country：Japan  

Event date： 2019.11

Language：English  

Country：Japan  

Event date： 2019.10

Language：English  

Country：China  

Event date： 2019.10

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：東京大学、東京   Country：Japan  

Event date： 2019.9

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：岡山大学、岡山   Country：Japan  

Event date： 2019.9

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：九州工業大学、福岡   Country：Japan  

Event date： 2019.9

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：九州工業大学、福岡   Country：Japan  

Event date： 2019.9

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：九州工業大学、福岡   Country：Japan  

Event date： 2019.9

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：岡山大学、岡山   Country：Japan  

Event date： 2019.9

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：岡山大学、岡山   Country：Japan  

Event date： 2019.9

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：岡山大学、岡山   Country：Japan  

Event date： 2019.7

Language：English   Presentation type：Oral presentation (general)  

Country：Germany  

Event date： 2019.7

Language：English  

Country：Germany  

Event date： 2019.7 - 2019.8

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：大阪府立国際会議場、大阪   Country：Japan  

Event date： 2019.7 - 2019.8

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2019.7 - 2019.8

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：大阪府立国際会議場、大阪   Country：Japan  

Event date： 2019.6

Language：Japanese  

Venue：神戸   Country：Japan  

Event date： 2019.6

Language：Japanese  

Venue：神戸   Country：Japan  

Event date： 2019.5

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：国立オリンピック記念青少年総合センター   Country：Japan  

Event date： 2019.5

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：国立オリンピック記念青少年総合センター   Country：Japan  

Event date： 2019.4

Language：English  

Country：Italy  

Event date： 2019.4

Language：Japanese  

Country：Japan  

Event date： 2019.4

Language：Japanese  

Country：Japan  

Event date： 2019.4

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2019.4

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2019.4

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2019.4

Language：English   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2019.4

Language：Japanese  

Country：Japan  

Event date： 2019.4

Language：English  

Country：United States  

Event date： 2019.4

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2019.4

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2019.4

Language：Japanese  

Country：Japan  

Event date： 2019.4

Language：Japanese  

Country：Japan  

Event date： 2019.4

Language：Japanese  

Country：Japan  

Event date： 2019.4

Language：Japanese  

Country：Japan  

Event date： 2019.4

Language：Japanese  

Country：Japan  

Event date： 2019.3

Language：English  

Country：United States  

Other Link： https://www.xcdsystem.com/uscap/program/2019/

Event date： 2019.2

Language：Japanese  

Venue：仙台   Country：Japan  

Event date： 2019.2

Language：Japanese  

Venue：仙台   Country：Japan  

Event date： 2017.10

Language：English  

Country：Italy  

Event date： 2017.10

Language：English   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2017.9

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：琉球大学   Country：Japan  

Event date： 2017.9

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：琉球大学   Country：Japan  

Event date： 2017.8

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：広島国際会議場   Country：Japan  

Event date： 2017.7

Language：English   Presentation type：Oral presentation (general)  

Country：United States  

Event date： 2017.6

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2017.5

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2017.4

Language：English  

Country：Japan  

Event date： 2017.4 - 2018.4

Language：English   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2016.9

Language：Japanese  

Country：Japan  

Event date： 2016.6 - 2016.7

Language：English  

Country：United States  

Event date： 2013.2

Language：English  

Country：Austria  

Event date： 2011.8 - 2011.9

Language：English  

Country：United States  

Event date： 2011.3 - 2011.4

Language：English  

Country：United States  

Event date： 2011.3 - 2011.4

Language：English  

Country：United States  

Event date： 2011.1

Language：English  

Country：United States  

Event date： 2010.4

Language：English  

Country：Netherlands  

Event date： 2010.4

Language：English  

Country：Netherlands  

Event date： 2002.7

Language：English  

Country：Germany  

Language：English  

Country：Japan  

Language：English  

Country：Japan  

Language：English  

Country：Other  

Language：Japanese  

Language：Japanese  

Language：Japanese  

Language：English  

Language：Japanese  

Language：Japanese  

Language：Japanese  

Language：Japanese  

Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (scientific journal)  

Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (scientific journal)  

Type：Competition, symposium, etc. 

Type：Competition, symposium, etc. 

Type：Competition, symposium, etc. 

Type：Competition, symposium, etc. 

Type：Competition, symposium, etc. 

Type：Competition, symposium, etc. 

Type：Peer review 

Number of peer-reviewed articles in foreign language journals：3

Proceedings of International Conference Number of peer-reviewed papers：18

Type：Competition, symposium, etc. 

Type：Competition, symposium, etc. 

Type：Competition, symposium, etc. 

Type：Competition, symposium, etc. 

Type：Peer review 

Number of peer-reviewed articles in foreign language journals：4

Proceedings of International Conference Number of peer-reviewed papers：20

Type：Competition, symposium, etc. 

Type：Competition, symposium, etc. 

Number of participants：7,500

Type：Competition, symposium, etc. 

Type：Peer review 

Number of peer-reviewed articles in foreign language journals：2

Proceedings of International Conference Number of peer-reviewed papers：14

Type：Competition, symposium, etc. 

Type：Peer review 

Number of peer-reviewed articles in foreign language journals：3

Number of peer-reviewed articles in Japanese journals：3

Proceedings of International Conference Number of peer-reviewed papers：16

Proceedings of domestic conference Number of peer-reviewed papers：2

Type：Competition, symposium, etc. 

Type：Competition, symposium, etc. 

Type：Peer review 

Number of peer-reviewed articles in foreign language journals：2

Number of peer-reviewed articles in Japanese journals：1

Proceedings of International Conference Number of peer-reviewed papers：9

Proceedings of domestic conference Number of peer-reviewed papers：1

Type：Peer review 

Number of peer-reviewed articles in foreign language journals：1

Number of peer-reviewed articles in Japanese journals：3

Proceedings of International Conference Number of peer-reviewed papers：13

Proceedings of domestic conference Number of peer-reviewed papers：0