A deep community based approach for large scale content based X-ray image retrieval |
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| Institution: | 1. The University of British Columbia, Vancouver, Canada;2. IBM Research - Almaden Research Center, San Jose, USA;1. Visual Computing Group, Department of Electrical and Computer Engineering, Democritus University of Thrace, 67100 Xanthi, Greece;2. Department of Medical Physics, School of Medicine, University of Patras, 26504 Patras, Greece;1. Department of Computer Engineering, University of Engineering and Technology, Taxila, Pakistan;2. Department of Software Engineering, University of Engineering and Technology, Taxila, Pakistan;3. Center for Vision, Speech and Signal Processing, University of Surrey, Surrey, UK;4. Signal, Image, Multimedia Processing and Learning (SIMPLE) Group, University of Engineering and Technology, Taxila, Pakistan;5. Cerebrai Artificial Intelligence, Surrey, UK |
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| Abstract: | A computer assisted system for automatic retrieval of medical images with similar image contents can serve as an efficient management tool for handling and mining large scale data, and can also be used as a tool in clinical decision support systems. In this paper, we propose a deep community based automated medical image retrieval framework for extracting similar images from a large scale X-ray database. The framework integrates a deep learning-based image feature generation approach and a network community detection technique to extract similar images. When compared with the state-of-the-art medical image retrieval techniques, the proposed approach demonstrated improved performance. We evaluated the performance of the proposed method on two large scale chest X-ray datasets, where given a query image, the proposed approach was able to extract images with similar disease labels with a precision of 85%. To the best of our knowledge, this is the first deep community based image retrieval application on large scale chest X-ray database. |
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