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The main aim of this book is to present a sample of recent research on the application of novel artificial intelligence paradigms to the diagnosis and prognosis of breast cancer. These paradigms include neural networks, fuzzy logic and evolutionary computing. Artificial intelligence techniques offer advantages — such as adaptation, fault tolerance, learning and human-like behavior — over conventional computing techniques. The idea is to combine the pathological, intelligent and statistical approaches to enable simple and accurate diagnosis and prognosis.
This book is the first of its kind on the topic of artificial intelligence in breast cancer. It presents the applications of artificial intelligence in breast cancer diagnosis and prognosis, and includes state-of-the-art concepts in the field. It contains contributions from Australia, Germany, Italy, UK and the USA.
Contents:
- An Introduction to Breast Cancer Diagnosis, Prognosis, and Artificial Intelligence (N Harbeck et al.)
- Automatic Image Feature Extraction for Diagnosis and Prognosis of Breast Cancer (M J Bottema et al.)
- Decision Support in Breast Cancer: Recent Advances in Prognostic and Predictive Techniques (R Kates et al.)
- MammoNet: A Bayesian Network Diagnosing Breast Cancer (L M Roberts)
- Predicting Prognosis and Treatment Response in Breast Cancer Patients (M G Daidone & D Coradini)
- Computer-Aided Breast Cancer Diagnosis (H-P Chan et al.)
- Which Decision Support Technologies are Appropriate for the Cytodiagnosis of Breast Cancer? (S S Cross et al.)
- Xcyt: A System for Remote Cytological Diagnosis and Prognosis of Breast Cancer (W N Street)
Readership: Medical practitioners, researchers and graduate students.
