​This book provides a complete overview of the role of machine learning in radiation oncology and medical physics, covering basic theory, methods, and a variety of applications in medical physics and radiotherapy. An introductory section explains machine learning, reviews supervised and unsupervised learning methods, discusses performance evaluation, and summarizes potential applications in radiation oncology. Detailed individual sections are then devoted to the use of machine learning in quality assurance; computer-aided detection, including treatment planning and contouring; image-guided radiotherapy; respiratory motion management; and treatment response modeling and outcome prediction. The book will be invaluable for students and residents in medical physics and radiation oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.

Cancer is a complex disease process that spans multiple scales in space and time. Driven by cutting-edge mathematical and computational techniques, in silico biology provides powerful tools to investigate the mechanistic relationships of genes, cells, and tissues. It enables the creation of experimentally testable hypotheses, the integration of dat

Cell Surface Proteases provides a comprehensive overview of these important enzymes that catalyze the hydrolysis of a protein as it degrades to a simpler substance. In the 1990s, an explosion of new discoveries shed light on the role of cell surface proteases and extended it beyond degradation of extracellular matrix components to include its influence on growth factors, cell signaling, and other cellular events. This volume unites the scientific literature from across disciplines and teases out unified themes of interactions between cell surface proteases and interconnecting cell surface-related systems -- including integrins and other adhesion molecules. Scientists and students involved in developmental biology, cell biology and disease processes will find this an indispensable resource.* Provides an overview of the entire field of cell surface proteases in a single volume* Presents major issues and astonishing discoveries at the forefront of modern developmental biology and developmental medicine * A thematic volume in the longest-running forum for contemporary issues in developmental biology with over 30 years of coverage

This book constitutes the refereed proceedings of the 17th Conference on Artificial Intelligence in Medicine, AIME 2019, held in Poznan, Poland, in June 2019. The 22 revised full and 31 short papers presented were carefully reviewed and selected from 134 submissions. The papers are organized in the following topical sections: deep learning; simulation; knowledge representation; probabilistic models; behavior monitoring; clustering, natural language processing, and decision support; feature selection; image processing; general machine learning; and unsupervised learning.

This unique book introduces a variety of techniques designed to represent, enhance and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. Providing a unique compendium of current and emerging machine learning paradigms for healthcare informatics, it reflects the diversity, complexity, and the depth and breadth of this multi-disciplinary area. Further, it describes techniques for applying machine learning within organizations and explains how to evaluate the efficacy, suitability, and efficiency of such applications. Featuring illustrative case studies, including how chronic disease is being redefined through patient-led data learning, the book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare challenges.

Since the last edition of this book, major advances have been made in our understanding of key pathways that control tumor progression. This has led to the development of new anticancer agents that have the ability to block the activity of proteins involved in neoplastic cell development and proliferation. Targeted Therapies in Oncology, Second Edition provides a concise timely panorama of existing targeted therapies and progress into future anticancer treatments. These therapies notably include: Targeted agents of immune checkpoints Signal-transduction inhibitors Antiangiogenic agents Vascular-disrupting agents Apoptosis modulators Stem cell inhibitors Tumor profiling for drug development The book emphasizes the biology behind this new class of drugs as well as the clinical achievements obtained. The contributors to this volume stand at the cutting edge of cancer research and treatment around the world.