This book combines technology and the medical domain. It covers advances in computer vision (CV) and machine learning (ML) that facilitate automation in diagnostics and therapeutic and preventive health care. The special focus on eXplainable Artificial Intelligence (XAI) uncovers the black box of ML and bridges the semantic gap between the technologists and the medical fraternity. Explainable AI in Healthcare: Unboxing Machine Learning for Biomedicine intends to be a premier reference for practitioners, researchers, and students at basic, intermediary levels and expert levels in computer science, electronics and communications, information technology, instrumentation and control, and electrical engineering.
This book will benefit readers in the following ways:
- Explores state of art in computer vision and deep learning in tandem to develop autonomous or semi-autonomous algorithms for diagnosis in health care
- Investigates bridges between computer scientists and physicians being built with XAI
- Focuses on how data analysis provides the rationale to deal with the challenges of healthcare and making decision-making more transparent
- Initiates discussions on human-AI relationships in health care
- Unites learning for privacy preservation in health care
Betal enkelt med kort, Klarna, Apple Pay eller Google Pay. Ikke fornøyd? Du har alltid 14 dagers angrerett. Les mer i våre vilkår. Har du spørsmål, send oss en e-post på hello@memmo.org.
Memmo gjør det enklere å studere – uansett hvor du er i verden. Hos oss samler du pensumbøker og smarte studieverktøy på ett og samme sted: sammendrag, quizer, podkaster og flashcards. Og så Ted, din studiekompis som svarer på alt du lurer på. Over 50 000 studenter studerer allerede her – bygget for at du skal lære raskere og stresse mindre.