Sara Colantonio – författare

This book constitutes the refereed proceedings of two workshops held at the 18th International Conference on Computer Analysis of Images and Patterns, CAIP 2019, held in Salerno, Italy, in September 2019: First Workshop on Deep-learning based Computer Vision for UAV, DL-UAV 2019, and the First Workshop on Visual Computing and Machine Learning for Biomedical Applications, ViMaBi 2019.The 12 papers presented in this volume were carefully reviewed and selected from 16 submissions and focus on all aspects of visual computing and machine learning for biomedical applications, and deep-learning based computer vision for UAV.

The book covers multiple aspects and challenges, from legal to technical and validation, in the emerging topic of AI in cancer imaging, bringing together the experience of top researchers and flagship projects.The aim of this book is to address the important questions: “How to design AI that is trustworthy”, and “How to validate AI trustworthiness” in the scope of AI for cancer imaging research. The book discusses overall considerations and the generation of a framework; preparing for trustworthy AI, including the data and metadata for quality, transparency and traceability; implementing trustworthy AI with algorithms and Decision Support Systems; and validating trustworthy AI.Chapters 2 and 3 are available open access under a Creative Commons Attribution 4.0 International License via Springerlink.This is an ideal resource for researchers from technical and clinical research sites, postgraduate students, and healthcare professionals in cancer imaging and beyond.

The book covers multiple aspects and challenges, from legal to technical and validation, in the emerging topic of AI in cancer imaging, bringing together the experience of top researchers and flagship projects.The aim of this book is to address the important questions: “How to design AI that is trustworthy”, and “How to validate AI trustworthiness” in the scope of AI for cancer imaging research. The book discusses overall considerations and the generation of a framework; preparing for trustworthy AI, including the data and metadata for quality, transparency and traceability; implementing trustworthy AI with algorithms and Decision Support Systems; and validating trustworthy AI.Chapters 2 and 3 are available open access under a Creative Commons Attribution 4.0 International License via Springerlink.This is an ideal resource for researchers from technical and clinical research sites, postgraduate students, and healthcare professionals in cancer imaging and beyond.