Stacked Autoencoders for Medical Image Search
This work addresses the challenge of efficient medical image search for healthcare professionals, but it is incremental as it applies an existing method (stacked autoencoders) to a specific domain.
The paper tackled the problem of retrieving relevant medical images from large datasets by proposing a feature extraction technique using stacked autoencoders to encode images into binary vectors, achieving a retrieval error of 376 for 1,733 test images with 74.61% compression on the IRMA dataset.
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%.