Suzuki Yuki

スズキユウキ | Suzuki Yuki

Affiliation and department：
Homepage URL (1)： https://github.com/ykszk

Research field (1)： Perceptual information processing

Research theme for competitive and other funds (5)：

2022 - 2026 人工知能による深層学習を利用した特発性側弯症の進行予測
2022 - 2025 Construction of Radiogenomics Prediction Model for Pathological Complete Response after Preoperative Chemotherapy for Breast Cancer
2020 - 2025 人工知能を活用したドパミン機能画像によるシヌクレノパチー早期診断システムの確立
2021 - 2024 超高精細CTの新しい肺癌画像解析法の確立:診断能に寄与する画像因子探索とAI解析
2021 - 2024 Development of a general-purpose computer-aided diagnosis system using VAE that can be used for a small number of cases

Papers (33)：

Daiki Nishigaki, Yuki Suzuki, Tadashi Watabe, Daisuke Katayama, Hiroki Kato, Tomohiro Wataya, Kosuke Kita, Junya Sato, Noriyuki Tomiyama, Shoji Kido. Vision transformer to differentiate between benign and malignant slices in 18F-FDG PET/CT. Scientific Reports. 2024. 14. 1
Keisuke Ninomiya, Masahiro Yanagawa, Mitsuko Tsubamoto, Yukihisa Sato, Yuki Suzuki, Akinori Hata, Noriko Kikuchi, Yuriko Yoshida, Kazuki Yamagata, Shuhei Doi, et al. Prediction of solid and micropapillary components in lung invasive adenocarcinoma: radiomics analysis from high-spatial-resolution CT data with 1024 matrix. Japanese journal of radiology. 2024
Kosuke Kita, Takahito Fujimori, Yuki Suzuki, Yuya Kanie, Shota Takenaka, Takashi Kaito, Takuyu Taki, Yuichiro Ukon, Masayuki Furuya, Hirokazu Saiwai, et al. Bimodal artificial intelligence using TabNet for differentiating spinal cord tumors-Integration of patient background information and images. iScience. 2023. 26. 10. 107900-107900
Yuta Suganuma, Atsushi Teramoto, Kuniaki Saito, Hiroshi Fujita, Yuki Suzuki, Noriyuki Tomiyama, Shoji Kido. Hybrid Multiple-Organ Segmentation Method Using Multiple U-Nets in PET/CT Images. Applied Sciences. 2023. 13. 19. 10765-10765
Junya Sato, Yuki Suzuki, Tomohiro Wataya, Daiki Nishigaki, Kosuke Kita, Kazuki Yamagata, Noriyuki Tomiyama, Shoji Kido. Anatomy-aware self-supervised learning for anomaly detection in chest radiographs. iScience. 2023. 26. 7. 107086-107086

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MISC (8)：

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Awards (1)：

2023/05 - Japanese Society for Medical and Biological Engineering Best Paper Award, Sakamoto Award Comparative Study of Vessel Detection Methods for Contrast Enhanced Computed Tomography: Effects of Convolutional Neural Network Architecture and Patch Size

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