2021
S. B. Alam and S. Kobashi, “Comprehensive Modeling of Neonatal Brain Image Generation for Disorder Development Onset Prediction Based on Generative Adversarial Networks”, Multidisciplinary Computational Anatomy, Springer, pp. 269-273, 2021.
B. Hossain and S. Kobashi, “Prediction of Personalized Postoperative Implanted Knee Kinematics with Statistical Temporal Modeling”, Multidisciplinary Computational Anatomy, Springer, pp. 275-281, 2021.
2020
F.P. Mahdi and S. Kobashi, “A Deep Learning Technique for Automatic Teeth Recognition in Dental Panoramic X-Ray Images Using Modified Palmer Notation System”, Lecture Notes on Data Engineering and Communications Technologies, Springer, pp. 1-11.
2018
Saadia Binte Alam, Syoji Kobashi , Jayaram K Udupa, "Fuzzy object growth model for neonatal brain MR understanding," in "Intelligent decision support systems for diagnosis of medical images", Springier, pp. 208-222, 2018.
小橋昌司, 多元計算解剖学の基礎と臨床への応用，「3.2．時空間統計形状モデルを用いた発達障害発症リスク評価」担当, 誠文堂新光社, 2018.
2017
S. B. Alam, S. Kobashi, and J. K. Udupa, "Fuzzy object growth model for neonatal brain MR understanding," in Intelligent decision support systems for diagnosis of medical images(Springer)
2014
H. Hata, S. Imawaki, K. Kuramoto, S. Kobashi, Y. Hata, “Ultrasonic Muscular Thickness Measurement in Temperature Variation,” Advanced Intelligent Systems Advances in Intelligent Systems and Computing Volume 268, Springer, pp. 65-75, 2014
T. Fujisawa, T. Egawa, K. Taniguchi, S. Kobashi, Y. Hata “An Energy Visualization by Camera Monitoring,” Advanced Intelligent Systems Advances in Intelligent Systems and Computing Volume 268, pp. 51-64, Springer, 2014
S. Kikuchi , Y. Kaku, K. Kuramoto, S. Kobashi, and Y. Hata, "Regional Analysis and Predictive Modeling for Asthmatic Attacks in Himeji City," Advanced Intelligent Systems, Vol. 268, pp 77-84, 2014.
2008
Y. Hata, S. Kobashi and H. Nakajima, "Medical and health management system of systems," in Mo Jamshidi (ed.), Systems of Systems Engineering: Principles and Applications in Computational Intelligence, CRC press, pp.233-250, 2008.
2006
Y. Hata, O. Ishikawa, S. Kobashi and K. Kondo, "Combination Rule of Normal Degrees on Automated Medical Diagnosis System (AMDS), in B. Reusch (ed.), Computational Intelligence, Theory and Applications, Advances in Soft Computing Seriese, Springer-Verag, pp.339-347, 2006.
Y. Hata, K. Iseri, S. Kobashi, K. Kondo and K. Taniguchi, "A Fuzzy Ultrasonic System for Estimating Degradation of Insulating Oil," Computational Intelligencs, Theory and Applications, (B. Reusch Ed.), Springer, pp. 733-740, 2006.
2006
Y. Hata, O. Ishikawa, S. Kobashi and K. Kondo, "Combination Rule of Normal Degrees on Automated Medical Diagnosis System (AMDS), in B. Reusch (ed.), Computational Intelligence, Theory and Applications, Advances in Soft Computing Seriese, Springer-Verag, pp.339-347, 2006.
2005
Y. Hata, O. Ishikawa, and S. Kobashi, "Combination Rule of Normal Degrees on Automated Medical Diagnosis System (AMDS)," Computational Intelligence, Theory and Applications, Springer, pp. 339-347, 2005.
2002
S. Kobashi, Y. Hata, M. Matsui, H. Kitagaki, E. Mori, and T. Kanagawa, "Automated volumetry of lateral entricles in 3-D SPGR MR images using physicians’ knowledge represented by fuzzy logic," CARS 2002 Computer Assisted Radiology and Surgery, Springer, p. 1028, 2002.
C. Yasuba, S. Kobashi, K. Kondo, Y. Hata, S. Imawaki, and M. Ishikawa, "Finding a Non-continuous Tube by Fuzzy Inference for Segmenting the MR Cholangiography Image," Medical Image Computing and Computer-Assisted Intervention — MICCAI 2002, pp. 28-35, 2002.
2001
S. Kobashi, Y. Hata, Y. T. Kitamura, T. Hayakata, and T. Yanagida, "Brain State Recognition Using Fuzzy C-Means (FCM) Clustering with Near Infrared Spectroscopy (NIRS)," Computational Intelligence. Theory and Applications, Springer, pp. 124-136, 2001.
1999
S. kobashi, S. Hirano and Y. Hata, "Automatic Human Brain MR Image Segmentation Based On Fuzzy Logic Techniques," in B. J. Vellas and J. L. Fitten (eds.),Research and Practice in Alzheimer's Disease, Springer.
1997
Y. Hata, S. Kobashi, N. Kamiura, and M. Ishikawa, "Fuzzy logic approach to 3D magnetic resonance image segmentation," Information Processing in Medical Imaging, Springer, pp. 387-392, 1997.