Parallel deep solutions for image retrieval from imbalanced medical imaging archives

[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 L...

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Vydáno v:Applied soft computing Ročník 63; s. 197 - 205
Hlavní autoři: Khatami, Amin, Babaie, Morteza, Khosravi, Abbas, Tizhoosh, H.R., Nahavandi, Saeid
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.02.2018
Témata:
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
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Popis
Shrnutí:[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.
ISSN:1568-4946
1872-9681
DOI:10.1016/j.asoc.2017.11.024