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- Author: Jie Tian
- Publisher: Academic Press
- ISBN-10: 012818101X
- ISBN-13: 9780128181010
- Format: 19.1 x 23.5 x 1.6 cm, softcover
- Language: English
- SAVE -10% with code: EXTRA
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Description
The rapid development of artificial intelligence technology in medical data analysis has led to the concept of radiomics. This book introduces the essential and latest technologies in radiomics, such as imaging segmentation, quantitative imaging feature extraction, and machine learning methods for model construction and performance evaluation, providing invaluable guidance for the researcher entering the field.
It fully describes three key aspects of radiomic clinical practice: precision diagnosis, the therapeutic effect, and prognostic evaluation, which make radiomics a powerful tool in the clinical setting.
This book is a very useful resource for scientists and computer engineers in machine learning and medical image analysis, scientists focusing on antineoplastic drugs, and radiologists, pathologists, oncologists, as well as surgeons wanting to understand radiomics and its potential in clinical practice.
EXTRA 10 % discount with code: EXTRA
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The discount code is valid when purchasing from 10 €. Discounts do not stack.
- Author: Jie Tian
- Publisher: Academic Press
- ISBN-10: 012818101X
- ISBN-13: 9780128181010
- Format: 19.1 x 23.5 x 1.6 cm, softcover
- Language: English English
The rapid development of artificial intelligence technology in medical data analysis has led to the concept of radiomics. This book introduces the essential and latest technologies in radiomics, such as imaging segmentation, quantitative imaging feature extraction, and machine learning methods for model construction and performance evaluation, providing invaluable guidance for the researcher entering the field.
It fully describes three key aspects of radiomic clinical practice: precision diagnosis, the therapeutic effect, and prognostic evaluation, which make radiomics a powerful tool in the clinical setting.
This book is a very useful resource for scientists and computer engineers in machine learning and medical image analysis, scientists focusing on antineoplastic drugs, and radiologists, pathologists, oncologists, as well as surgeons wanting to understand radiomics and its potential in clinical practice.
Reviews