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Mathematical and Computational Oncology : First International Symposium, ISMCO 2019, Lake Tahoe, NV, USA, October 14-16, 2019, Proceedings / edited by George Bebis, Takis Benos, Ken Chen, Katharina Jahn, Ernesto Lima.
- Format:
- Book
- Series:
- Computer Science (SpringerNature-11645)
- LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics ; SL 6, 11826
- Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 11826
- Language:
- English
- Subjects (All):
- Computer vision.
- Software engineering.
- Artificial intelligence.
- Computer science-Mathematics.
- Mathematical statistics.
- Computer Vision.
- Software Engineering.
- Artificial Intelligence.
- Probability and Statistics in Computer Science.
- Local Subjects:
- Computer Vision.
- Software Engineering.
- Artificial Intelligence.
- Probability and Statistics in Computer Science.
- Physical Description:
- 1 online resource (XXIII, 99 pages) : 117 illustrations, 26 illustrations in color.
- Edition:
- 1st ed. 2019.
- Contained In:
- Springer Nature eBook
- Place of Publication:
- Cham : Springer International Publishing : Imprint: Springer, 2019.
- System Details:
- text file PDF
- Summary:
- This book constitutes the refereed proceedings of the First International Symposium on Mathematical and Computational Oncology, ISMCO'2019, held in Lake Tahoe, NV, USA, in October 2019. The 7 full papers presented were carefully reviewed and selected from 30 submissions. The papers are organized in topical sections named: Tumor evolvability and intra-tumor heterogeneity; Imaging and scientific visualization for cancer research; Statistical methods and data mining for cancer research (SMDM); Spatio-temporal tumor modeling and simulation (STTMS).
- Contents:
- Special Track: Tumor evolvability and intra-tumor heterogeneity
- Imaging and scientific visualization for cancer research
- Statistical methods and data mining for cancer research (SMDM)
- Spatio-temporal tumor modeling and simulation (STTMS).
- Other Format:
- Printed edition:
- ISBN:
- 978-3-030-35210-3
- 9783030352103
- Access Restriction:
- Restricted for use by site license.
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