Journal's Facebook pageConvolutional Neural Networks and Hash Learning for Feature Extraction and of Fast Retrieval of Pulmonary Nodules
- School of Data Science and Technology, North University of China
Taiyuan 030051, China
6833330@qq.com, {qpl, Zhangk, chairui}@nuc.edu.cn
Abstract
With a widespread use of digital imaging data in hospitals, the size of medical image repositories is increasing rapidly. This causes difficulty in managing and querying these large databases leading to the need of content based medical image retrieval (CBMIR) systems. A major challenge in CBMIR systems is the “semantic gap” that exists between the low level visual information captured by imaging devices and high level semantic information perceived by the human. Using deep convolution neural network (CNN) to construct the CBMIR system can fully characterize the high level semantic features information for medical image retrieval. The existing network mostly used for the natural images can’t produce a good result directly applied to medical image. This paper used UNet method to preprocessing under the guidance of medical knowledge. Then, multi-scale receiving field convolution module is used to extract features of the segmented images with different sizes. Finally, encoded the features and used a coarse to fine search strategy with an average search accuracy of 0.73.
Key words
Content Based Medical Image Retrieval (CBMIR), Convolutional Neural Networks (CNN), Similarity Measure, Deep Learning
Digital Object Identifier (DOI)
https://doi.org/10.2298/CSIS171210020Q
Publication information
Volume 15, Issue 3 (October 2018)
Year of Publication: 2018
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium
Full text
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How to cite
Qin, P., Chen, J., Zhang, K., Chai, R.: Convolutional Neural Networks and Hash Learning for Feature Extraction and of Fast Retrieval of Pulmonary Nodules. Computer Science and Information Systems, Vol. 15, No. 3, 517–531. (2018), https://doi.org/10.2298/CSIS171210020Q
