Parallel deep solutions for image retrieval from imbalanced medical imaging archives

• Published in: Applied soft computing Vol. 63; pp. 197 - 205
• Main Authors: Khatami, Amin, Babaie, Morteza, Khosravi, Abbas, Tizhoosh, H.R., Nahavandi, Saeid
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.02.2018
• Subjects: CBIR; Content-based image retrieval; Deep learning; HOG; LBP; Medical imaging; Radon; Deep learning; HOG; Medical imaging; Radon; CBIR; Content-based image retrieval; LBP
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2017.11.024

[Display omitted] •Propose a generic scheme, using deep CNNs from classification domain to retrieval.•Propose a combination of deep networks which results in a shrunken search space.•The shrunken search space enables a robust local similarity-based search phase.•The retrieval system are subject to LBP, HOG, and Radon features.•The proposed retrieval model surpasses all the methods reported in the literature. 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.