@inproceedings{30e9f60b3a334acfb497891b5e66e0f2,
title = "Stacked autoencoders for medical image search",
abstract = "Medical images can be a valuable resource for reliable information to support medical diagnosis. However, the large volume of medical images makes it challenging to retrieve relevant information given a particular scenario. To solve this challenge, content-based image retrieval (CBIR) attempts to characterize images (or image regions) with invariant content information in order to facilitate image search. This work presents a feature extraction technique for medical images using stacked autoencoders, which encode images to binary vectors. The technique is applied to the IRMA dataset, a collection of 14,410 x-ray images in order to demonstrate the ability of autoencoders to retrieve similar x-rays given test queries. Using IRMA dataset as a benchmark, it was found that stacked autoencoders gave excellent results with a retrieval error of 376 for 1,733 test images with a compression of 74.61%.",
author = "S. Sharma and I. Umar and L. Ospina and D. Wong and Tizhoosh, {H. R.}",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 12th International Symposium on Visual Computing, ISVC 2016 ; Conference date: 12-12-2016 Through 14-12-2016",
year = "2016",
doi = "10.1007/978-3-319-50835-1_5",
language = "English (US)",
isbn = "9783319508344",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "45--54",
editor = "George Bebis and Bahram Parvin and Sandra Skaff and Daisuke Iwai and Richard Boyle and Darko Koracin and Fatih Porikli and Carlos Scheidegger and Alireza Entezari and Jianyuan Min and Amela Sadagic and Tobias Isenberg",
booktitle = "Advances in Visual Computing - 12th International Symposium, ISVC 2016, Proceedings",
}