Stacked Autoencoders for Medical Image Search [article]

S. Sharma, I. Umar, L. Ospina, D. Wong, H.R. Tizhoosh
2016 arXiv   pre-print
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
more » ... ders, 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%.
arXiv:1610.00320v1 fatcat:lteaffvi6jf5re4d2bnu6lk3ki