@inproceedings{c867a00e5fd54eb1ac981e57c9d1b804,
title = "X-ray categorization and spatial localization of chest pathologies",
abstract = "In this study we present an efficient image categorization system for medical image databases utilizing a local patch representation based on both content and location. The system discriminates between healthy and pathological cases and indicates the subregion in the image that is automatically found to be most relevant for the decision. We show an application to pathology-level categorization of chest x-ray data, the most popular examination in radiology. Experimental results are provided on chest radiographs taken from routine hospital examinations.",
keywords = "Computer-Aided Diagnosis (CAD), Image categorization, chest radiography, region-of-interest (ROI), visual words, x-ray",
author = "Uri Avni and Hayit Greenspan and Jacob Goldberger",
year = "2011",
doi = "10.1007/978-3-642-23626-6_25",
language = "الإنجليزيّة",
isbn = "9783642236259",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
number = "PART 3",
pages = "199--206",
booktitle = "Medical Image Computing and Computer-Assisted Intervention, MICCAI 2011 - 14th International Conference, Proceedings",
edition = "PART 3",
note = "14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011 ; Conference date: 18-09-2011 Through 22-09-2011",
}