Stacked autoencoders for medical image search

S. Sharma, I. Umar, L. Ospina, D. Wong, H. R. Tizhoosh

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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%.

Original languageEnglish (US)
Title of host publicationAdvances in Visual Computing - 12th International Symposium, ISVC 2016, Proceedings
EditorsGeorge Bebis, Bahram Parvin, Sandra Skaff, Daisuke Iwai, Richard Boyle, Darko Koracin, Fatih Porikli, Carlos Scheidegger, Alireza Entezari, Jianyuan Min, Amela Sadagic, Tobias Isenberg
PublisherSpringer Verlag
Number of pages10
ISBN (Print)9783319508344
StatePublished - 2016
Event12th International Symposium on Visual Computing, ISVC 2016 - Las Vegas, United States
Duration: Dec 12 2016Dec 14 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10072 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference12th International Symposium on Visual Computing, ISVC 2016
Country/TerritoryUnited States
CityLas Vegas

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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