Artificial Intelligence in Cancer Diagnostic to Tailored Treatment
Auteur : Belciug Smaranda
Artificial Intelligence in Cancer: Diagnostic to Tailored Treatment provides theoretical concepts and practical techniques of AI and its applications in cancer management, building a roadmap on how to use AI in cancer at different stages of healthcare. It discusses topics such as the impactful role of AI during diagnosis and how it can support clinicians to make better decisions, AI tools to help pathologists identify exact types of cancer, how AI supports tumor profiling and can assist surgeons, and the gains in precision for oncologists using AI tools. Additionally, it provides information on AI used for survival and remission/recurrence analysis.
The book is a valuable source for bioinformaticians, cancer researchers, oncologists, clinicians and members of the biomedical field who want to understand the promising field of AI applications in cancer management.
Her research includes Artificial Intelligence, Machine Learning, Data Mining and Statistics focusing on cancer research. She authored 5 books, 3 book chapters and more than 25 scientific papers published in prestigious international journals. She won a special mention at the Young Researchers in Science and Technology contest, from Prof. Rada Mihalcea, University of Michigan.
- Discusses over 20 real cancer examples, bringing state-of-the-art cancer cases in which AI was used to help the medical personnel
- Presents over 100 diagrams, making it easier to comprehend AI’s results on a specific problem through visual resources
- Explains AI algorithms in a friendly manner, thus helping the reader implement or use them in a specific cancer case
Date de parution : 06-2020
Ouvrage de 318 p.
19x23.3 cm
Thème d’Artificial Intelligence in Cancer :
Mots-clés :
3D printing; 3D tumor reconstruction; Adversarial attacks; Artificial neural networks; Autonomous surgery; Bayes’ theorem; Benefits of free smartphone apps; Big Pharma; Cancer history; Chemotherapy; Classification; Classification and decision trees; Clinical trials; Cluster networks; Clustering; Clustering analysis; Cox regression model; Deep learning; Digital pathology; Dosimetry; Entropy; Evolutionary computation; Exponential distribution; Generalized Gamma distribution; Genome project; GINI index; Gompertz distribution; Hazard ratio; Hormonal therapy; Human genome; Hunt's algorithm; Hypothesis testing; Image analysis; Imagistics; Immunotherapy; Kaplan-Meier; Learning paradigms; Life tables; Log logistic distribution; Log normal distribution; Log rank test; Logistic regression; Misclassification error; Natural language processing; Palliative care; Pattern recognition; Power analysis; Precision medicine; preoperative planning; Radiation therapy; Radiomics; Random tree forest; Recurrence; Remission; Segmentation; Self organizing maps; Side effects; Snake oil; Statistical assessment; Statistical parameters; Statistical tables; Statistical tests; Supervised learning; Support vector machines; Unsupervised learning; Virtual reality; Weibull distribution