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Erschienen in:
Anti-occlusion Light-Field Optical Flow Estimation Using Light-Field Super-Pixels Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

Auto-classification of Retinal Diseases in the Limit of Sparse Data Using a Two-Streams Machine Learning Model

verfasst von : C.-H. Huck Yang, Fangyu Liu, Jia-Hong Huang, Meng Tian, M. D. I-Hung Lin, Yi Chieh Liu, Hiromasa Morikawa, Hao-Hsiang Yang, Jesper Tegnèr

Erschienen in: Computer Vision – ACCV 2018 Workshops

Verlag: Springer International Publishing

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Abstract

Automatic clinical diagnosis of retinal diseases has emerged as a promising approach to facilitate discovery in areas with limited access to specialists. Based on the fact that fundus structure and vascular disorders are the main characteristics of retinal diseases, we propose a novel visual-assisted diagnosis hybrid model mixing the support vector machine (SVM) and deep neural networks (DNNs). Furthermore, we present a new clinical retina labels collection sorted by the professional ophthalmologist from the educational project Retina Image Bank, called EyeNet, for ophthalmology incorporating 52 retina diseases classes. Using EyeNet, our model achieves 90.40% diagnosis accuracy, and the model performance is comparable to the professional ophthalmologists (https://​github.​com/​huckiyang/​EyeNet2).

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Vorheriges Kapitel OCT Segmentation via Deep Learning: A Review of Recent Work
Nächstes Kapitel LoANs: Weakly Supervised Object Detection with Localizer Assessor Networks
Literatur
1.
Tan, O., et al.: Detection of macular ganglion cell loss in glaucoma by Fourier-domain optical coherence tomography. Ophthalmology 116, 2305–2314 (2009)CrossRef
2.
Lalezary, M., et al.: Baseline optical coherence tomography predicts the development of glaucomatous change in glaucoma suspects. Am. J. Ophthalmol. 142, 576–582 (2006)CrossRef
3.
Sharifi, M., Fathy, M., Mahmoudi, M.T.: A classified and comparative study of edge detection algorithms. In: International Conference on Information Technology: Coding and Computing, Proceedings, pp. 117–120. IEEE (2002)
4.
Abràmoff, M.D., Garvin, M.K., Sonka, M.: Retinal imaging and image analysis. Rev. Biomed. Eng. 3, 169–208 (2010)CrossRef
5.
Pizzarello, L., et al.: Vision 2020: the right to sight: a global initiative to eliminate avoidable blindness. Arch. Ophthalmol. 122, 615–620 (2004)CrossRef
6.
Bhattacharya, S.: Watermarking digital images using fuzzy matrix compositions and (\(\alpha \), \(\beta \))-cut of fuzzy set. Int. J. Adv. Comput. 5, 135 (2014)
7.
Lin, C.Y., Wu, M., Bloom, J.A., Cox, I.J., Miller, M.L., Lui, Y.M.: Rotation-, scale-, and translation-resilient public watermarking for images. In: Security and Watermarking of Multimedia Contents II, vol. 3971, pp. 90–99. International Society for Optics and Photonics (2000)
8.
Cochocki, A., Unbehauen, R.: Neural Networks for Optimization and Signal Processing. Wiley, New York (1993)
9.
10.
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 248–255. IEEE (2009)
11.
Rajpurkar, P., Zhang, J., Lopyrev, K., Liang, P.: Squad: 100,000+ questions for machine comprehension of text. arXiv preprint arXiv:​1606.​05250 (2016)
12.
Antol, S., et al.: VQA: Visual question answering. In: Proceedings of the ICCV, pp. 2425–2433 (2015)
13.
14.
Huang, J.H., Dao, C.D., Alfadly, M., Ghanem, B.: A novel framework for robustness analysis of visual qa models. arXiv:​1711.​06232 (2017)
15.
16.
Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115 (2017)CrossRef
17.
Gulshan, V., et al.: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316, 2402–2410 (2016)CrossRef
18.
Rajpurkar, P., Hannun, A.Y., Haghpanahi, M., Bourn, C., Ng, A.Y.: Cardiologist-level arrhythmia detection with convolutional neural networks. arXiv preprint arXiv:​1707.​01836 (2017)
19.
Rajpurkar, P., et al.: CheXNet: Radiologist-level pneumonia detection on chest X-rays with deep learning. arXiv preprint arXiv:​1711.​05225 (2017)
20.
Grewal, M., Srivastava, M.M., Kumar, P., Varadarajan, S.: RADNET: radiologist level accuracy using deep learning for hemorrhage detection in ct scans. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 281–284. IEEE (2018)
21.
Bejnordi, B.E., et al.: Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 318, 2199–2210 (2017)CrossRef
22.
Gale, W., Oakden-Rayner, L., Carneiro, G., Bradley, A.P., Palmer, L.J.: Detecting hip fractures with radiologist-level performance using deep neural networks. arXiv preprint arXiv:​1711.​06504 (2017)
23.
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.M.: ChestX-ray8: hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3462–3471. IEEE (2017)
24.
Staal, J., Abràmoff, M.D., Niemeijer, M., Viergever, M.A., Van Ginneken, B.: Ridge-based vessel segmentation in color images of the retina. TMI 23, 501–509 (2004)
25.
Gertych, A., Zhang, A., Sayre, J., Pospiech-Kurkowska, S., Huang, H.: Bone age assessment of children using a digital hand atlas. Comput. Med. Imaging Graph. 31, 322–331 (2007)CrossRef
26.
Rajpurkar, P., et al.: Mura dataset: towards radiologist-level abnormality detection in musculoskeletal radiographs. arXiv preprint arXiv:​1712.​06957 (2017)
27.
Heath, M., Bowyer, K., Kopans, D., Moore, R., Kegelmeyer, P.: The digital database for screening mammography. In: Digital Mammography, pp. 431–434 (2000)
28.
Costa, J.A., Hero, A.: Classification constrained dimensionality reduction. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005. Proceedings, (ICASSP’05), vol. 5, pp. 1077. IEEE (2005)
29.
Fukunaga, K.: Introduction to Statistical Pattern Recognition. Academic Press, London (2013)MATH
30.
Jimenez, L.O., Landgrebe, D.A.: Supervised classification in high-dimensional space: geometrical, statistical, and asymptotical properties of multivariate data. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 28, 39–54 (1998)CrossRef
31.
Yang, Z., He, X., Gao, J., Deng, L., Smola, A.: Stacked attention networks for image question answering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 21–29 (2016)
32.
Trier, Ø.D., Jain, A.K., Taxt, T.: Feature extraction methods for character recognition-a survey. Pattern Recogn. 29, 641–662 (1996)CrossRef
33.
Srihari, R., Li, W.: Information extraction supported question answering. Technical report, Cymfony Net Inc., Williamsville NY (1999)
34.
Somers, H.: Example-based machine translation. Mach. Transl. 14, 113–157 (1999)CrossRef
35.
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:​1409.​0473 (2014)
36.
Ciresan, D., Giusti, A., Gambardella, L.M., Schmidhuber, J.: Deep neural networks segment neuronal membranes in electron microscopy images. In: Advances in neural information processing systems, pp. 2843–2851 (2012)
37.
Hariharan, B., Arbeláez, P., Girshick, R., Malik, J.: Hypercolumns for object segmentation and fine-grained localization. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 447–456 (2015)
38.
Seyedhosseini, M., Sajjadi, M., Tasdizen, T.: Image segmentation with cascaded hierarchical models and logistic disjunctive normal networks. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp. 2168–2175. IEEE(2013)
39.
40.
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3431–3440 (2015)
41.
42.
Khurana, A.: Comprehensive Ophthalmology. New Age International Ltd. (2007)
43.
Rezaee, K., Haddadnia, J., Tashk, A.: Optimized clinical segmentation of retinal blood vessels by using combination of adaptive filtering, fuzzy entropy and skeletonization. Appl. Soft Comput. 52, 937–951 (2017)CrossRef
44.
Turk, M.A., Pentland, A.P.: Face recognition using eigenfaces. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Proceedings CVPR 1991, pp. 586–591. IEEE (1991)
45.
Lyons, M.J., Budynek, J., Akamatsu, S.: Automatic classification of single facial images. IEEE Trans. Pattern Anal. Mach. Intell. 21, 1357–1362 (1999)CrossRef
46.
Wu, J., Zhou, Z.H.: Face recognition with one training image per person. Pattern Recogn. Lett. 23, 1711–1719 (2002)CrossRef
47.
Moghaddam, B., Wahid, W., Pentland, A.: Beyond eigenfaces: probabilistic matching for face recognition. In: Third IEEE International Conference on Automatic Face and Gesture Recognition, 1998. Proceedings, pp. 30–35. IEEE (1998)
48.
Akram, M.U., Tariq, A., Khan, S.A.: Retinal recognition: Personal identification using blood vessels. In: 2011 International Conference for Internet Technology and Secured Transactions (ICITST), pp. 180–184. IEEE (2011)
49.
Kuo, B.C., Ho, H.H., Li, C.H., Hung, C.C., Taur, J.S.: A kernel-based feature selection method for SVM with RBF kernel for hyperspectral image classification. IEEE J.Sel. Top. Appl. Earth Observ. Remote Sens. 7, 317–326 (2014)CrossRef
50.
Crick, R.P., Khaw, P.T.: A Textbook of Clinical Ophthalmology: A Practical Guide to Disorders of the Eyes and Their Management. World Scientific
51.
Akram, I., Rubinstein, A.: Common retinal signs. an overview. Optometry Today (2005)
52.
Tang, S., Huang, L., Wang, Y., Wang, Y.: Contrast-enhanced ultrasonography diagnosis of fundal localized type of gallbladder adenomyomatosis. BMC Gastroenterol. 15, 99 (2015)CrossRef
53.
Noyel, G., Thomas, R., Bhakta, G., Crowder, A., Owens, D., Boyle, P.: Superimposition of eye fundus images for longitudinal analysis from large public health databases. Biomed. Phys. Eng. Express 3, 045015 (2017)CrossRef
54.
55.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in NIPS, pp. 1097–1105 (2012)
56.
Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: CVPR, pp. 5987–5995. IEEE (2017)
57.
Nair, V., Hinton, G.E.: Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML-2010), pp. 807–814 (2010)
58.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition
59.
Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., Keutzer, K.: SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and \(<\)0.5 MB model size. arXiv:​1602.​07360 (2016)
60.
Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? In: NIPS, pp. 3320–3328 (2014)
61.
Yang, C.H.H., et al.: A novel hybrid machine learning model for auto-classification of retinal diseases. arXiv preprint arXiv:​1806.​06423 (2018)
62.
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. TIST 2, 27 (2011)CrossRef
63.
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: CVPR (2016)
Metadaten
Titel
Auto-classification of Retinal Diseases in the Limit of Sparse Data Using a Two-Streams Machine Learning Model
verfasst von
C.-H. Huck Yang
Fangyu Liu
Jia-Hong Huang
Meng Tian
M. D. I-Hung Lin
Yi Chieh Liu
Hiromasa Morikawa
Hao-Hsiang Yang
Jesper Tegnèr
Copyright-Jahr
2019
Buch
Computer Vision – ACCV 2018 Workshops

Print ISBN: 978-3-030-21073-1

Electronic ISBN: 978-3-030-21074-8

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-21074-8_28

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