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The science of solving clinical problems by analyzing images generated in clinical practice is known as medical image analysis.
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, R. Salakhutdinov, Dropout: a simple way to prevent neural networks from overfitting, The Journal of Machine Learning Research 15 (1) (2014) 1929–1958
1958
Earlier work this paper cites.
N.-S. Chang, K.-S. Fu, Query-by-pictorial-example, IEEE Transactions on Software Engineering (6) (1980) 519–524
1980
Earlier work this paper cites.
Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document recognition, Proceedings of the IEEE 86 (11) (1998) 2278–2324
1998
Earlier work this paper cites.
A. Heidenreich, F. Desgrandschamps, F. Terrier, Modern approach of diagnosis and management of acute flank pain: review of all imaging modalities, European urology 41 (4) (2002) 351–362
2002
Earlier work this paper cites.
H. Müller, A. Rosset, J.-P. Vallée, F. Terrier, A. Geissbuhler, A reference data set for the evaluation of medical image retrieval systems, Computerized Medical Imaging and Graphics 28 (6) (2004) 295–305
2004
Earlier work this paper cites.
H. Müller, N. Michoux, D. Bandon, A. Geissbuhler, A review of content-based image retrieval systems in medical applications—clinical benefits and future directions, International journal of medical informatics 73 (1) (2004) 1–23
2004
Earlier work this paper cites.
D. Brahmi, D. Ziou, Improving cbir systems by integrating semantic features, in: Computer and Robot Vision, 2004. Proceedings. First Canadian Conference on, IEEE, 2004, pp. 233–240
2004
Earlier work this paper cites.
M. M. Rahman, B. C. Desai, P. Bhattacharya, Medical image retrieval with probabilistic multi-class support vector machine classifiers and adaptive similarity fusion, Computerized Medical Imaging and Graphics 32 (2) (2008) 95–108
2008
Earlier work this paper cites.
M. J. Gangeh, L. Sørensen, S. B. Shaker, M. S. Kamel, M. De Bruijne, M. Loog, A texton-based approach for the classification of lung parenchyma in ct images, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, 2010, pp. 595–602
2010
Earlier work this paper cites.
L. Sorensen, S. B. Shaker, M. De Bruijne, Quantitative analysis of pulmonary emphysema using local binary patterns, IEEE transactions on medical imaging 29 (2) (2010) 559–569
2010
Earlier work this paper cites.
X.-F. Diao, X.-Y. Zhang, T.-F. Wang, S.-P. Chen, Y. Yang, L. Zhong, Highly sensitive computer aided diagnosis system for breast tumor based on color doppler flow images, Journal of medical systems 35 (5) (2011) 801–809
2011
Earlier work this paper cites.
Y. Tao, Z. Peng, A. Krishnan, X. S. Zhou, Robust learning-based parsing and annotation of medical radiographs, IEEE transactions on medical imaging 30 (2) (2011) 338–350
2011
Earlier work this paper cites.
L. Zhang, Q. Ji, A bayesian network model for automatic and interactive image segmentation, IEEE Transactions on Image Processing 20 (9) (2011) 2582–2593
2011
Earlier work this paper cites.
M. M. Rahman, S. K. Antani, G. R. Thoma, A learning-based similarity fusion and filtering approach for biomedical image retrieval using svm classification and relevance feedback, IEEE Transactions on Information Technology in Biomedicine 15 (4) (2011) 640–646
2011
Earlier work this paper cites.
Y. Liu, H. Cheng, J. Huang, Y. Zhang, X. Tang, J.-W. Tian, Y. Wang, Computer aided diagnosis system for breast cancer based on color doppler flow imaging, Journal of medical systems 36 (6) (2012) 3975–3982
2012
Earlier work this paper cites.
K. H. Hwang, H. Lee, D. Choi, Medical image retrieval: past and present, Healthcare informatics research 18 (1) (2012) 3–9
2012
Earlier work this paper cites.
M. Mizotin, J. Benois-Pineau, M. Allard, G. Catheline, Feature-based brain mri retrieval for alzheimer disease diagnosis, in: Image Processing (ICIP), 2012 19th IEEE International Conference on, IEEE, 2012, pp. 1241–1244
2012
Earlier work this paper cites.
J. Wan, D. Wang, S. C. H. Hoi, P. Wu, J. Zhu, Y. Zhang, J. Li, Deep learning for content-based image retrieval: A comprehensive study, in: Proceedings of the 22nd ACM international conference on Multimedia, ACM, 2014, pp. 157–166
2014
Earlier work this paper cites.
L. Deng, D. Yu, et al., Deep learning: methods and applications, Foundations and Trends® in Signal Processing 7 (3–4) (2014) 197–387
2014
Earlier work this paper cites.
Y. Gao, Y. Zhan, D. Shen, Incremental learning with selective memory (ilsm): Towards fast prostate localization for image guided radiotherapy, IEEE transactions on medical imaging 33 (2) (2014) 518–534
2014
Earlier work this paper cites.
G. W. Jiji, P. S. J. D. Raj, Content-based image retrieval in dermatology using intelligent technique, IET Image Processing 9 (4) (2014) 306–317
2014
Earlier work this paper cites.
L. Deng, D. Yu, et al., Deep learning: methods and applications, Foundations and Trends® in Signal Processing 7 (3–4) (2014) 197–387
2014
Earlier work this paper cites.
M. Anthimopoulos, S. Christodoulidis, A. Christe, S. Mougiakakou, Classification of interstitial lung disease patterns using local dct features and random forest, in: Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, IEEE, 2014, pp. 6040–6043
2014
Earlier work this paper cites.
M. S. Miri, M. D. Abràmoff, K. Lee, M. Niemeijer, J.-K. Wang, Y. H. Kwon, M. K. Garvin, Multimodal segmentation of optic disc and cup from sd-oct and color fundus photographs using a machine-learning graph-based approach, IEEE transactions on medical imaging 34 (9) (2015) 1854–1866
2015
Earlier work this paper cites.
C. Mosquera-Lopez, S. Agaian, A. Velez-Hoyos, I. Thompson, Computer-aided prostate cancer diagnosis from digitized histopathology: a review on texture-based systems, IEEE reviews in biomedical engineering 8 (2015) 98–113
2015
Earlier work this paper cites.
J. Torrents-Barrena, P. Lazar, R. Jayapathy, M. Rathnam, B. Mohandhas, D. Puig, Complex wavelet algorithm for computer-aided diagnosis of alzheimer’s disease, Electronics Letters 51 (20) (2015) 1566–1568
2015
Earlier work this paper cites.
A. A. Salam, M. U. Akram, K. Wazir, S. M. Anwar, M. Majid, Autonomous glaucoma detection from fundus image using cup to disc ratio and hybrid features, in: Signal Processing and Information Technology (ISSPIT), 2015 IEEE International Symposium on, IEEE, 2015, pp. 370–374
2015
Earlier work this paper cites.
A. A. Salam, M. U. Akram, S. Abbas, S. M. Anwar, Optic disc localization using local vessel based features and support vector machine, in: Bioinformatics and Bioengineering (BIBE), 2015 IEEE 15th International Conference on, IEEE, 2015, pp. 1–6
2015
Earlier work this paper cites.
M. S. Thakur, M. Singh, Content based image retrieval using line edge singular value pattern (lesvp): A review paper, International Journal of Advanced Research in Computer Science and Software Engineering 5 (3) (2015) 648–652
2015
Earlier work this paper cites.
Y. LeCun, Y. Bengio, G. Hinton, Deep learning, nature 521 (7553) (2015) 436
2015
Cited alongside, same era.
S. Ding, L. Lin, G. Wang, H. Chao, Deep feature learning with relative distance comparison for person re-identification, Pattern Recognition 48 (10) (2015) 2993–3003
2015
Cited alongside, same era.
H. Greenspan, B. van Ginneken, R. M. Summers, Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique, IEEE Transactions on Medical Imaging 35 (5) (2016) 1153–1159
2016
Cited alongside, same era.
G. Wang, A perspective on deep imaging, IEEE Access 4 (2016) 8914–8924
2016
Cited alongside, same era.
S. Shi, Q. Wang, P. Xu, X. Chu, Benchmarking state-of-the-art deep learning software tools, in: Cloud Computing and Big Data (CCBD), 2016 7th International Conference on, IEEE, 2016, pp. 99–104
2016
A. Jenitta, R. S. Ravindran, Image retrieval based on local mesh vector co-occurrence pattern for medical diagnosis from mri brain images, Journal of medical systems 41 (10) (2017) 157
2017
Closest in time.
Y. Feng, H. Zhao, X. Li, X. Zhang, H. Li, A multi-scale 3d otsu thresholding algorithm for medical image segmentation, Digital Signal Processing 60 (2017) 186–199
2017
Closest in time.
D. Gupta, R. Anand, A hybrid edge-based segmentation approach for ultrasound medical images, Biomedical Signal Processing and Control 31 (2017) 116–126
2017
Closest in time.
I. Cabria, I. Gondra, Mri segmentation fusion for brain tumor detection, Information Fusion 36 (2017) 1–9
2017
Closest in time.
K. B. Soulami, M. N. Saidi, A. Tamtaoui, A cad system for the detection of abnormalities in the mammograms using the metaheuristic algorithm particle swarm optimization (pso), in: Advances in Ubiquitous Networking 2, Springer, 2017, pp. 505–517
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2016
Cited alongside, same era.
G. Vishnuvarthanan, M. P. Rajasekaran, P. Subbaraj, A. Vishnuvarthanan, An unsupervised learning method with a clustering approach for tumor identification and tissue segmentation in magnetic resonance brain images, Applied Soft Computing 38 (2016) 190–212
2016
Cited alongside, same era.
T. von Landesberger, D. Basgier, M. Becker, Comparative local quality assessment of 3d medical image segmentations with focus on statistical shape model-based algorithms, IEEE transactions on visualization and computer graphics 22 (12) (2016) 2537–2549
2016
Cited alongside, same era.
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2016
Cited alongside, same era.
H.-Y. Ma, Z. Zhou, S. Wu, Y.-L. Wan, P.-H. Tsui, A computer-aided diagnosis scheme for detection of fatty liver in vivo based on ultrasound kurtosis imaging, Journal of medical systems 40 (1) (2016) 33
2016
Cited alongside, same era.
B. Remeseiro, A. Mosquera, M. G. Penedo, Casdes: a computer-aided system to support dry eye diagnosis based on tear film maps, IEEE journal of biomedical and health informatics 20 (3) (2016) 936–943
2016
Cited alongside, same era.
M. Saha, R. Mukherjee, C. Chakraborty, Computer-aided diagnosis of breast cancer using cytological images: a systematic review, Tissue and Cell 48 (5) (2016) 461–474
2016
Cited alongside, same era.
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2017
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2017
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2017
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2017
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2017
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2017
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2017
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2017
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2017
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2017
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2017
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2018
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2018
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2018
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2018
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2018
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2018
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2018
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2018
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