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http://dx.doi.org/10.3837/tiis.2022.07.013

An Effective WSSENet-Based Similarity Retrieval Method of Large Lung CT Image Databases  

Zhuang, Yi (School of Computer & Information Engineering, Zhejiang Gongshang University)
Chen, Shuai (School of Computer & Information Engineering, Zhejiang Gongshang University)
Jiang, Nan (Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine)
Hu, Hua (Hangzhou Normal University)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.16, no.7, 2022 , pp. 2359-2376 More about this Journal
Abstract
With the exponential growth of medical image big data represented by high-resolution CT images(CTI), the high-resolution CTI data is of great importance for clinical research and diagnosis. The paper takes lung CTI as an example to study. Retrieving answer CTIs similar to the input one from the large-scale lung CTI database can effectively assist physicians to diagnose. Compared with the conventional content-based image retrieval(CBIR) methods, the CBIR for lung CTIs demands higher retrieval accuracy in both the contour shape and the internal details of the organ. In traditional supervised deep learning networks, the learning of the network relies on the labeling of CTIs which is a very time-consuming task. To address this issue, the paper proposes a Weakly Supervised Similarity Evaluation Network (WSSENet) for efficiently support similarity analysis of lung CTIs. We conducted extensive experiments to verify the effectiveness of the WSSENet based on which the CBIR is performed.
Keywords
CT images; spatial transformer layer; deep learning; content-based medical image retrieval;
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1 H. Lai, et al, "Simultaneous feature learning and hash coding with deep neural networks," in Proc. of the IEEE conf. on computer vision and pattern recognition, 2015.
2 A. Khatami, et al, "A deep-structural medical image classification for a radon-based image retrieval," in Proc. of 2017 IEEE 30th Canadian Conf. on Electrical and Computer Engineering (CCECE), 2017.
3 A. Khatami, M. Babaie, HR. Tizhoosh, A. Khosravi, T. Nguyen, S. Nahavandi, "A sequential search-space shrinking using CNN transfer learning and a Radon projection pool for medical image retrieval," Expert Systems with Applications, vol. 100, pp. 224-233, 2018.   DOI
4 L. Ma, et al, "A new method of content based medical image retrieval and its applications to CT imaging sign retrieval," J. of biomedical informatics, vol. 66, pp. 148-158, 2017.   DOI
5 H. Liu, et al, "Deep supervised hashing for fast image retrieval," in Proc. of the IEEE conf. on computer vision and pattern recognition, 2016.
6 YH. Cai, et al, "Medical image retrieval based on convolutional neural network and supervised hashing," IEEE access, vol. 7, pp. 51877-51885, 2019.   DOI
7 M. Jaderberg, K. Simonyan, A. Zisserman, K. Kavukcuoglu, "Spatial transformer networks," in Proc. of NIPS, pp. 2017-2025, 2015.
8 A. Vaswani, et al, "Attention is all you need," in Proc. of NIPS, pp. 6000-6010, 2017.
9 A. Dosovitskiy, et al, "An image is worth 16x16 words: Transformers for image recognition at scale," arXiv preprint arXiv:2010.11929, 2020.
10 SK. Sundararaja, B Sankaragomathi, DS Priya, "Deep belief CNN feature representation based content based image retrieval for medical images," J. of Medical Systems, 43(6), 1-9, 2019.   DOI
11 FL. Bookstein, "Principal warps: thin-plate splines and the decomposition of transformations," IEEE Trans. on Pattern Analysis and Machine Intelligence, 11(6), 567-585, 1989.   DOI
12 K. He, et al, "Deep residual learning for image recognition," in Proc. of the IEEE CVPR, 2016.
13 A.A.A. Setio, A. Traverso, T. de Belo, et al, "Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge," Medical Image Analysis, vol. 42, pp. 1-13, 2017.   DOI
14 N. Sampathila, and R J Martis, "Computational approach for content-based image retrieval of K-similar images from brain MR image database," Expert Systems, vol. 39, no. 7, e12652, 2022.
15 DG Lowe, "Distinctive image features from scale-invariant keypoints," Int'l J. of Computer Vision, 60(2), 91-110, 2004.   DOI
16 H. Muller, J. Kalpathy-Cramer, et al, "Overview of the CLEF 2009 medical image retrieval track," in Proc. of Workshop of the Cross-Language Evaluation Forum for European, pp. 72-84, 2009.
17 A. Krizhevsky, I Sutskever, and GE. Hinton, "Imagenet classification with deep convolutional neural networks," Communications of the ACM, vol. 60, no. 6, pp. 84-90, 2017.   DOI
18 H. Pan, P. Li, Q. Li, Q. Han, X. Feng, L. Gao, "Brain CT image similarity retrieval method based on uncertain location graph," IEEE J. of Biomedical and Health Informatics, 18(2), 574-584, 2014.   DOI
19 K. Karthik, and S.S. Kamath, "A hybrid feature modeling approach for content-based medical image retrieval," in Proc. of 2018 IEEE 13th Int'l Conf. on Industrial and Information Systems(ICIIS), 2018.
20 HC. Shin, HR. Roth, M. Gao, et al, "Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning," IEEE Trans. on Medical Imaging, 35(5), 1285-1298, 2016.   DOI
21 M Maxim, et al, "Feature-based brain MRI retrieval for Alzheimer disease diagnosis," in Proc. of IEEE ICIP, 2013.
22 ZH. Zhou, "A brief introduction to weakly supervised learning," National Science Review, 5(1), 2018, 44-53, 2018.   DOI