記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1007/s00138-024-01527-1

Web of Science

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その他リンク： https://link.springer.com/article/10.1007/s00138-024-01527-1/fulltext.html

記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1007/s10207-024-00827-x

Web of Science

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その他リンク： https://link.springer.com/article/10.1007/s10207-024-00827-x/fulltext.html

記述言語：英語   掲載種別：研究論文（学術雑誌）  

<jats:title>Abstract</jats:title><jats:p>The big Artificial General Intelligence models inspire hot topics currently. The black box problems of Artificial Intelligence (AI) models still exist and need to be solved urgently, especially in the medical area. Therefore, transparent and reliable AI models with small data are also urgently necessary. To build a trustable AI model with small data, we proposed a prior knowledge-integrated transformer model. We first acquired prior knowledge using Shapley Additive exPlanations from various pre-trained machine learning models. Then, we used the prior knowledge to construct the transformer models and compared our proposed models with the Feature Tokenization Transformer model and other classification models. We tested our proposed model on three open datasets and one non-open public dataset in Japan to confirm the feasibility of our proposed methodology. Our results certified that knowledge-integrated transformer models perform better (1%) than general transformer models. Meanwhile, our proposed methodology identified that the self-attention of factors in our proposed transformer models is nearly the same, which needs to be explored in future work. Moreover, our research inspires future endeavors in exploring transparent small AI models.</jats:p>

DOI： 10.1007/s41870-023-01635-7

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.2139/ssrn.4966114

Web of Science

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：SPIE-Intl Soc Optical Eng  

DOI： 10.1117/1.jbo.28.5.056501

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1007/s41870-023-01270-2

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

BACKGROUND: Hyperspectral imaging (HSI) is an emerging modality for the gross pathology of the skin. Spectral signatures of HSI could discriminate malignant from benign tissue. Because of inherent redundancies in HSI and in order to facilitate the use of deep-learning models, dimension reduction is a common preprocessing step. The effects of dimension reduction choice, training scope, and number of retained dimensions have not been evaluated on skin HSI for segmentation tasks. MATERIALS AND METHODS: An in-house dataset of HSI signatures from pigmented skin lesions was prepared and labeled with histology. Eleven different dimension reduction methods were used as preprocessing for tumor margin detection with support vector machines. Cluster-wise principal component analysis (ClusterPCA), a new variant of PCA, was proposed. The scope of application for dimension reduction was also investigated. RESULTS: The components produced by ClusterPCA show good agreement with the expected optical properties of skin chromophores. Random forest importance performed best during classification. However, all methods suffered from low sensitivity and generalization. CONCLUSION: Investigation of more complex reduction and segmentation schemes with emphasis on the nature of HSI and optical properties of the skin is necessary. Insights on dimension reduction for skin tissue could facilitate the development of HSI-based systems for cancer margin detection at gross level.

DOI： 10.1111/srt.13270

PubMed

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

PURPOSE: Diabetes mellitus causes various problems in our life. With the big data boom in our society, some risk factors for Diabetes must still exist. To identify new risk factors for diabetes in the big data society and explore further efficient use of big data, the non-objective-oriented census data about the Japanese Citizen's Survey of Living Conditions were analyzed using interpretable machine learning methods. METHODS: Seven interpretable machine learning methods were used to analysis Japan citizens' census data. Firstly, logistic analysis was used to analyze the risk factors of diabetes from 19 selected initial elements. Then, the linear analysis, linear discriminate analysis, Hayashi's quantification analysis method 2, random forest, XGBoost, and SHAP methods were used to re-check and find the different factor contributions. Finally, the relationship among the factors was analyzed to understand the relationship among factors. RESULTS: Four new risk factors: the number of family members, insurance type, public pension type, and health awareness level, were found as risk factors for diabetes mellitus for the first time, while another 11 risk factors were reconfirmed in this analysis. Especially the insurance type factor and health awareness level factor make more contributions to diabetes than factors: hypertension, hyperlipidemia, and stress in some interpretable models. We also found that work years were identified as a risk factor for diabetes because it has a high coefficient with the risk factor of age. CONCLUSIONS: New risk factors for diabetes mellitus were identified based on Japan's non-objective-oriented anonymous census data using interpretable machine learning models. The newly identified risk factors inspire new possible policies for preventing diabetes. Moreover, our analysis certifies that big data can help us find helpful knowledge in today's prosperous society. Our study also paves the way for identifying more risk factors and promoting the efficiency of using big data.

DOI： 10.1007/s12553-023-00730-w

PubMed

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