@inproceedings{84481d30ecfd4ffcb38e9f703d10e412,
title = "Machine learning meets medical imaging: Learning and discovery of clinically useful information from images",
abstract = "Three-dimensional (3D) and four-dimensional (4D) imaging plays an increasingly important role in computer-assisted diagnosis, intervention and therapy. However, in many cases the interpretation of these images is heavily dependent on the subjective assessment of the imaging data by clinicians. Over the last decades image registration has transformed the clinical workflow in many areas of medical imaging. At the same time, advances in machine learning have transformed many of the classical problems in computer vision into machine learning problems. This paper will focus on the convergence of image registration and machine learning techniques for the discovery and quantification of clinically useful information from medical images. We will illustrate this with several examples such as the segmentation of neuro-anatomical structures, the discovery of biomarkers for neurodegenerative diseases and the quantification of temporal changes such as atrophy in Alzheimer{\textquoteright}s disease.",
author = "Daniel Rueckert and Robin Wolz and Paul Aljabar",
note = "Publisher Copyright: {\textcopyright} 2014 Taylor & Francis Group, London.; 4th Eccomas Thematic Conference on Computational Vision and Medical Image Processing, VIPIMAGE 2013 ; Conference date: 14-10-2013 Through 16-10-2014",
year = "2014",
language = "English",
isbn = "9781138000810",
series = "Computational Vision and Medical Image Processing IV - Proceedings of Eccomas Thematic Conference on Computational Vision and Medical Image Processing, VIPIMAGE 2013",
publisher = "CRC Press/Balkema",
pages = "3--8",
editor = "Jo{\~a}o Manuel and R.S. Tavares and Jorge, {R.M. Natal}",
booktitle = "Computational Vision and Medical Image Processing IV - Proceedings of Eccomas Thematic Conference on Computational Vision and Medical Image Processing, VIPIMAGE 2013",
}