File(s) under permanent embargo

Parallel deep solutions for image retrieval from imbalanced medical imaging archives

journal contribution
posted on 2018-02-01, 00:00 authored by Seyedamin Khatami, M Babaie, Abbas KhosraviAbbas Khosravi, H R Tizhoosh, Saeid NahavandiSaeid Nahavandi
Learning and extracting representative features along with similarity measurements in high dimensional feature spaces is a critical task. Moreover, the problem of how to bridge the semantic gap, between the low-level information captured by a machine learning model and the high-level one interpreted by a human operator, is still a practical challenge, especially in medicine. In medical applications, retrieving similar images from archives of past cases can be immensely beneficial in diagnostic imaging. However, large and balanced datasets may not be available for many reasons. Exploring the ways of using deep networks, for classification to retrieval, to fill this semantic gap was a key question for this research. In this work, we propose a parallel deep solution approach based on convolutional neural networks followed by a local search using LBP, HOG and Radon features. The IRMA dataset, from ImageCLEF initiative, containing 14,400 X-ray images, is employed to validate the proposed scheme. With a total IRMA error of 165.55, the performance of our scheme surpasses the dictionary approach and many other learning methods applied on the same dataset.



Applied soft computing




197 - 205




Amsterdam, The Netherlands





Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2017, Elsevier