«This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging».
For smart mobility to be cost-efficient and ready for future needs, adequate research and innovation (R&I) in this field is necessary. This report provides a comprehensive analysis of R&I in smart mobility and services in Europe.
Research and innovation in smart mobility and services in Europe Leer más »
I will discuss relevant AI thrusts at NIST on health care informatics, focusing on the use of machine learning, knowledge representation and natural language processing. I will also discuss the need for explanations in AI systems (XAI) and current state of the art in medical XAI.
This book complements Shiny’s online documentation and is intended to help app authors develop a deeper understanding of Shiny. After reading this book, you’ll be able to write apps that have more customized UI, more maintainable code, and better performance and scalability.
With OpenMined, an AI model can be governed by multiple owners and trained securely on an unseen, distributed dataset.The mission of the OpenMined community is to create an accessible ecosystem of tools for private, secure, multi-owner governed AI
OpenMined: open source to make privacy-preserving of AI technologies Leer más »
The structure and content of this work has been guided by the curricula developed by the European Society of Radiology, the Royal College of Radiologists, the Alliance of Medical Student Educators in Radiology, with guidance and input from Canadian Radiology Undergraduate Education Coordinators, and the Canadian Heads of Academic Radiology (CHAR).
The MONAI framework is the open-source foundation being created by Project MONAI. MONAI is a freely available, community-supported, PyTorch-based framework for deep learning in healthcare imaging. It provides domain-optimized foundational capabilities for developing healthcare imaging training workflows in a native PyTorch paradigm.
Medical Open Network for AI (MONAI), AI Toolkit for Healthcare Imaging Leer más »
Following visible successes on a wide range of predictive tasks, machine learning techniques are attracting substantial interest from medical researchers and clinicians. We address the need for capacity development in this area by providing a conceptual introduction to machine learning alongside a practical guide to developing and evaluating predictive algorithms using freely-available open source software and public domain data
Machine learning in medicine: a practical introduction Leer más »
Learn the basics of secure and private AI techniques, including federated learning and secure multi-party computation. In this talk, Andrew Trask of OpenMined highlights the importance of privacy preserving machine learning, and how to use privacy-focused tools like PySyft.
The book focuses on machine learning models for tabular data (also called relational or structured data) and less on computer vision and natural language processing tasks. Reading the book is recommended for machine learning practitioners, data scientists, statisticians, and anyone else interested in making machine learning models interpretable.
Interpretable Machine Learning (A Guide for Making Black Box Models Explainable) Leer más »