Yamakawa Makoto

ヤマカワマコト | Yamakawa Makoto

Affiliation and department：
Homepage URL (1)： https://shiina-lab.hs.med.kyoto-u.ac.jp/

Research field (1)： Medical systems

Research keywords (5)： Photoacoustic Imaging , Deep Learning , 癌鑑別診断 , Tissue Viscoelasticity Imaging , Ultrasound

Research theme for competitive and other funds (9)：

2022 - 2025 Quantitative evaluation of the importance of training data quality in computer-aided diagnosis using liver tumor ultrasound images
2016 - 2020 Development of advanced photoacoustic imaging system based on integration of optics and ultrasound and control of optical phase space
2013 - 2016 Ultrasound viscoelastography: development of quantitative imaging of tissue viscoelasticity based on fusion of static and dynamic tissue excitation
2013 - 2015 Coded pulse excitation for enhanced ultrafast ultrasound imaging
2013 - 2015 Development of tissue viscoelasticity imaging technology based on high frame rate ultrasound imaging method

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Papers (41)：

Taisei Tosaki, Makoto Yamakawa, Tsuyoshi Shiina. A study on the optimal condition of ground truth area for liver tumor detection in ultrasound images using deep learning. Journal of Medical Ultrasonics. 2023. 50. 2. 167-176
Riwa Kishimoto, Mikio Suga, Masashi Usumura, Hiroko Iijima, Masahiro Yoshida, Hiroyuki Hachiya, Tsuyoshi Shiina, Makoto Yamakawa, Kei Konno, Takayuki Obata, et al. Shear wave speed measurement bias in a viscoelastic phantom across six ultrasound elastography systems: a comparative study with transient elastography and magnetic resonance elastography. Journal of medical ultrasonics (2001). 2022. 49. 2. 143-152
Naoshi Nishida, Makoto Yamakawa, Tsuyoshi Shiina, Yoshito Mekada, Mutsumi Nishida, Naoya Sakamoto, Takashi Nishimura, Hiroko Iijima, Toshiko Hirai, Ken Takahashi, et al. Artificial intelligence (AI) models for the ultrasonographic diagnosis of liver tumors and comparison of diagnostic accuracies between AI and human experts. Journal of gastroenterology. 2022. 57. 4. 309-321
Naotaka Nitta, Makoto Yamakawa, Hiroyuki Hachiya, Tsuyoshi Shiina. A review of physical and engineering factors potentially affecting shear wave elastography. Journal of medical ultrasonics (2001). 2021. 48. 4. 403-414
Makoto Yamakawa, Tsuyoshi Shiina, Naoshi Nishida, Masatoshi Kudo. Optimal cropping for input images used in a convolutional neural network for ultrasonic diagnosis of liver tumors. JAPANESE JOURNAL OF APPLIED PHYSICS. 2020. 59

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MISC (370)：

椎名毅, 山川誠, 浪田健. 高機能心腔内超音波イメージング法の開発. 芝浦工業大学特別教育・研究報告集(CD-ROM). 2023. 2022
山川誠, 山川誠, 神田実穂, 大島萌, 浪田健, 浪田健, 椎名毅, 椎名毅. Basic Study of Optimal Training Data Creation Conditions For Computer-Aided Diagnosis using Ultrasound Images of Breast Tumors. 電子情報通信学会技術研究報告(Web). 2022. 122. 192(US2022 37-42)
浪田健, 浪田健, 吉田奈央, 山川誠, 山川誠, 椎名毅, 椎名毅. Anisakis detection by multiple-wavelength analysis of photoacoustic images. レーザー学会研究会報告. 2022. 569th
西田直生志, 山川誠, 目加田慶人, 椎名毅, 工藤正俊. 【AIの足音は肝胆膵診療に聞こえてきたか!】肝臓学とAI 超音波画像でのAIを用いた肝腫瘤検出と鑑別診断. 肝胆膵. 2022. 84. 1. 37-45
木内那由, 浪田健, 近藤健悟, 山川誠, 椎名毅. 光音響イメージングによる脂肪率定量評価法の基礎的検討. 電子情報通信学会技術研究報告(超音波). 2021. 121. 182. 18-22

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Patents (17)：

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Books (5)：

Lectures and oral presentations (71)：

超音波画像による肝腫瘤診断支援AI開発
(日本超音波医学会第96回学術集会 2023)
AMED事業における乳腺腫瘤の超音波診断支援AI開発
(日本超音波医学会第96回学術集会 2023)
超音波画像を用いた肝腫瘤鑑別診断AIについての基礎検討
(日本超音波医学会第34回関東甲信越地方学術集会 2022)
乳腺腫瘤超音波画像診断支援AI開発における最適な教師データ作成条件の基礎的検討
(電子情報通信学会2022年度第5回超音波研究会 2022)
AMED事業によるJSUM超音波画像データベース構築とAI開発
(2022)

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Education (2)：

Professional career (1)：

Work history (7)：

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Committee career (18)：

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Awards (7)：

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Association Membership(s) (5)：

日本超音波医学会 , 電子情報通信学会 , IEEE , 日本乳腺甲状腺超音波医学会 , 電気学会

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