Abstract

We present an algorithm for identifying retinal pathologies given retinal optical coherence tomography (OCT) images. Our approach fine-tunes a pre-trained convolutional neural network (CNN), GoogLeNet, to improve its prediction capability (compared to random initialization training) and identifies salient responses during prediction to understand learned filter characteristics. We considered a data set containing subjects with diabetic macular edema, or dry age-related macular degeneration, or no pathology. The fine-tuned CNN could effectively identify pathologies in comparison to classical learning. Our algorithm aims to demonstrate that models trained on non-medical images can be fine-tuned for classifying OCT images with limited training data.

© 2017 Optical Society of America

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    [Crossref] [PubMed]
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2016 (3)

2015 (2)

2014 (10)

A. Carass, A. Lang, M. Hauser, P. A. Calabresi, H. S. Ying, and J. L. Prince, “Multiple-object geometric deformable model for segmentation of macular OCT,” Biomed. Opt. Express 5(4), 1062–1074 (2014).
[Crossref] [PubMed]

P. P. Srinivasan, S. J. Heflin, J. A. Izatt, V. Y. Arshavsky, and S. Farsiu, “Automatic segmentation of up to ten layer boundaries in SD-OCT images of the mouse retina with and without missing layers due to pathology,” Biomed. Opt. Express 5(2), 348–365 (2014).
[Crossref] [PubMed]

R. R. Bourne, J. B. Jonas, S. R. Flaxman, J. Keeffe, J. Leasher, K. Naidoo, M. B. Parodi, K. Pesudovs, H. Price, R. A. White, T. Y. Wong, S. Resnikoff, H. R. Taylor, and Vision Loss Expert Group of the Global Burden of Disease Study, “Prevalence and causes of vision loss in high-income countries and in Eastern and Central Europe: 1990-2010,” Br. J. Ophthalmol. 98(5), 629–638 (2014).
[Crossref] [PubMed]

S. Farsiu, S. J. Chiu, R. V. O’Connell, F. A. Folgar, E. Yuan, J. A. Izatt, C. A. Toth, and Age-Related Eye Disease Study 2 Ancillary Spectral Domain Optical Coherence Tomography Study Group, “Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography,” Ophthalmology 121(1), 162–172 (2014).
[Crossref] [PubMed]

M. Oquab, L. Bottou, I. Laptev, and J. Sivic, “Weakly supervised object recognition with convolutional neural networks,” Adv. Neural Inf. Process. Syst. 2014, 1717–1724 (2014).

J. Yosinki, J. Clune, Y. Bengio, and H. Lipson, “How transferable are features in deep neural networks?” Adv. Neural Inf. Process. Syst. 2014, 3320–3328 (2014).

N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. 15(1), 1929–1958 (2014).

F. Rathke, S. Schmidt, and C. Schnörr, “Probabilistic intra-retinal layer segmentation in 3-D OCT images using global shape regularization,” Med. Image Anal. 18(5), 781–794 (2014).
[Crossref] [PubMed]

Y. Zhang, B. Zhang, F. Coenen, J. Xiao, and W. Lu, “One-class kernel subspace ensemble for medical image classification,” EURASIP J. Adv. Signal Process. 2014, 1–13 (2014).

P. P. Srinivasan, L. A. Kim, P. S. Mettu, S. W. Cousins, G. M. Comer, J. A. Izatt, and S. Farsiu, “Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images,” Biomed. Opt. Express 5(10), 3568–3577 (2014).
[Crossref] [PubMed]

2013 (5)

Y. Bengio, A. Courville, and P. Vincent, “Representation learning: A review and new perspectives,” IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013).
[Crossref] [PubMed]

P. Romero-Aroca, “Current status in diabetic macular edema treatments,” World J. Diabetes 4(5), 165–169 (2013).
[PubMed]

C. B. Rickman, S. Farsiu, C. A. Toth, and M. Klingeborn, “Dry age-related macular degeneration: Mechanisms, therapeutic targets, and imaging dry AMD mechanisms, targets, and imaging,” Investigative Ophthalmol. Vis. Sci. 54, ORSF68 (2013).

R. Kafieh, H. Rabbani, M. D. Abramoff, and M. Sonka, “Intra-retinal layer segmentation of 3D optical coherence tomography using coarse grained diffusion map,” Med. Image Anal. 17(8), 907–928 (2013).
[Crossref] [PubMed]

P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schröder, S. De Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
[Crossref] [PubMed]

2012 (5)

S. J. Chiu, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, and S. Farsiu, “Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images,” Invest. Ophthalmol. Vis. Sci. 53(1), 53–61 (2012).
[Crossref] [PubMed]

M. A. Mayer, A. Borsdorf, M. Wagner, J. Hornegger, C. Y. Mardin, and R. P. Tornow, “Wavelet denoising of multiframe optical coherence tomography data,” Biomed. Opt. Express 3(3), 572–589 (2012).
[Crossref] [PubMed]

S. Ding, H. Zhu, W. Jia, and C. Su, “A survey on feature extraction for pattern recognition,” Artif. Intell. Rev. 37(3), 169–180 (2012).
[Crossref]

G. Mesnil, Y. Dauphin, X. Glorot, S. Rifai, Y. Bengio, I. J. Goodfellow, E. Lavoie, X. Muller, G. Desjardins, D. Warde-Farley, and P. Vincent, “Unsupervised and transfer learning challenge: A deep learning approach,” ICML Unsupervised and Transfer Learning 27, 97–110 (2012).

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Adv. Neural Inf. Process. Syst. 2012, 1097–1105 (2012).

2011 (4)

E. Tátrai, S. Ranganathan, M. Ferencz, D. C. DeBuc, and G. M. Somfai, “Comparison of retinal thickness by Fourier-domain optical coherence tomography and OCT retinal image analysis software segmentation analysis derived from Stratus optical coherence tomography images,” J. Biomed. Opt. 16(5), 056004 (2011).
[Crossref] [PubMed]

J.-C. Mwanza, J. D. Oakley, D. L. Budenz, R. T. Chang, O. J. Knight, and W. J. Feuer, “Macular ganglion cell-inner plexiform layer: automated detection and thickness reproducibility with spectral domain-optical coherence tomography in glaucoma,” Invest. Ophthalmol. Vis. Sci. 52(11), 8323–8329 (2011).
[Crossref] [PubMed]

G. Gregori, F. Wang, P. J. Rosenfeld, Z. Yehoshua, N. Z. Gregori, B. J. Lujan, C. A. Puliafito, and W. J. Feuer, “Spectral domain optical coherence tomography imaging of drusen in nonexudative age-related macular degeneration,” Ophthalmology 118(7), 1373–1379 (2011).
[PubMed]

J. Duchi, E. Hazan, and Y. Singer, “Adaptive subgradient methods for online learning and stochastic optimization,” J. Mach. Learn. Res. 12, 2121–2159 (2011).

2010 (1)

S. J. Pan and Q. Yang, “A survey on transfer learning,” IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010).
[Crossref]

2008 (2)

Y. L. Boureau and Y. L. Cun, “Sparse feature learning for deep belief networks,” Adv. Neural Inf. Process. Syst. 2008, 1185–1192 (2008).

W. Drexler and J. G. Fujimoto, “State-of-the-art retinal optical coherence tomography,” Prog. Retin. Eye Res. 27(1), 45–88 (2008).
[Crossref] [PubMed]

2007 (1)

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. Image Process. 16(8), 2080–2095 (2007).
[Crossref] [PubMed]

2003 (2)

T. A. Ciulla, A. G. Amador, and B. Zinman, “Diabetic retinopathy and diabetic macular edema: pathophysiology, screening, and novel therapies,” Diabetes Care 26(9), 2653–2664 (2003).
[Crossref] [PubMed]

A. Kanamori, M. Nakamura, M. F. Escano, R. Seya, H. Maeda, and A. Negi, “Evaluation of the glaucomatous damage on retinal nerve fiber layer thickness measured by optical coherence tomography,” Am. J. Ophthalmol. 135(4), 513–520 (2003).
[Crossref] [PubMed]

1999 (1)

T. Otani, S. Kishi, and Y. Maruyama, “Patterns of diabetic macular edema with optical coherence tomography,” Am. J. Ophthalmol. 127(6), 688–693 (1999).
[Crossref] [PubMed]

1998 (1)

Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE 86(11), 2278–2324 (1998).
[Crossref]

Abdillahi, H.

P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schröder, S. De Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
[Crossref] [PubMed]

Abramoff, M. D.

R. Kafieh, H. Rabbani, M. D. Abramoff, and M. Sonka, “Intra-retinal layer segmentation of 3D optical coherence tomography using coarse grained diffusion map,” Med. Image Anal. 17(8), 907–928 (2013).
[Crossref] [PubMed]

Allingham, M. J.

Amador, A. G.

T. A. Ciulla, A. G. Amador, and B. Zinman, “Diabetic retinopathy and diabetic macular edema: pathophysiology, screening, and novel therapies,” Diabetes Care 26(9), 2653–2664 (2003).
[Crossref] [PubMed]

Anguelov, D.

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 1–9.

Arshavsky, V. Y.

Bengio, Y.

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521(7553), 436–444 (2015).
[Crossref] [PubMed]

J. Yosinki, J. Clune, Y. Bengio, and H. Lipson, “How transferable are features in deep neural networks?” Adv. Neural Inf. Process. Syst. 2014, 3320–3328 (2014).

Y. Bengio, A. Courville, and P. Vincent, “Representation learning: A review and new perspectives,” IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013).
[Crossref] [PubMed]

G. Mesnil, Y. Dauphin, X. Glorot, S. Rifai, Y. Bengio, I. J. Goodfellow, E. Lavoie, X. Muller, G. Desjardins, D. Warde-Farley, and P. Vincent, “Unsupervised and transfer learning challenge: A deep learning approach,” ICML Unsupervised and Transfer Learning 27, 97–110 (2012).

Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE 86(11), 2278–2324 (1998).
[Crossref]

X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feedforward neural networks,” Proc. AISTATS9,249–256 (2010).

Borsdorf, A.

Bottou, L.

M. Oquab, L. Bottou, I. Laptev, and J. Sivic, “Weakly supervised object recognition with convolutional neural networks,” Adv. Neural Inf. Process. Syst. 2014, 1717–1724 (2014).

Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE 86(11), 2278–2324 (1998).
[Crossref]

Boureau, Y. L.

Y. L. Boureau and Y. L. Cun, “Sparse feature learning for deep belief networks,” Adv. Neural Inf. Process. Syst. 2008, 1185–1192 (2008).

Bourne, R. R.

R. R. Bourne, J. B. Jonas, S. R. Flaxman, J. Keeffe, J. Leasher, K. Naidoo, M. B. Parodi, K. Pesudovs, H. Price, R. A. White, T. Y. Wong, S. Resnikoff, H. R. Taylor, and Vision Loss Expert Group of the Global Burden of Disease Study, “Prevalence and causes of vision loss in high-income countries and in Eastern and Central Europe: 1990-2010,” Br. J. Ophthalmol. 98(5), 629–638 (2014).
[Crossref] [PubMed]

Budenz, D. L.

J.-C. Mwanza, J. D. Oakley, D. L. Budenz, R. T. Chang, O. J. Knight, and W. J. Feuer, “Macular ganglion cell-inner plexiform layer: automated detection and thickness reproducibility with spectral domain-optical coherence tomography in glaucoma,” Invest. Ophthalmol. Vis. Sci. 52(11), 8323–8329 (2011).
[Crossref] [PubMed]

Calabresi, P. A.

Carass, A.

Ceklic, L.

P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schröder, S. De Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
[Crossref] [PubMed]

Chakraborthi, D.

Chang, R. T.

J.-C. Mwanza, J. D. Oakley, D. L. Budenz, R. T. Chang, O. J. Knight, and W. J. Feuer, “Macular ganglion cell-inner plexiform layer: automated detection and thickness reproducibility with spectral domain-optical coherence tomography in glaucoma,” Invest. Ophthalmol. Vis. Sci. 52(11), 8323–8329 (2011).
[Crossref] [PubMed]

Chatterjee, J.

Cheung, C. Y.

G. Lemaître, M. Rastgoo, J. Massich, C. Y. Cheung, T. Y. Wong, E. Lamoureux, D. Milea, F. Mériaudeau, and D. Sidibé, “Classification of SD-OCT volumes using local binary patterns: Experimental Validation for DME detection,” J. Ophthalmol. 2016, 3298606 (2016).
[Crossref] [PubMed]

Chiu, S. J.

S. J. Chiu, M. J. Allingham, P. S. Mettu, S. W. Cousins, J. A. Izatt, and S. Farsiu, “Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema,” Biomed. Opt. Express 6(4), 1172–1194 (2015).
[Crossref] [PubMed]

S. Farsiu, S. J. Chiu, R. V. O’Connell, F. A. Folgar, E. Yuan, J. A. Izatt, C. A. Toth, and Age-Related Eye Disease Study 2 Ancillary Spectral Domain Optical Coherence Tomography Study Group, “Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography,” Ophthalmology 121(1), 162–172 (2014).
[Crossref] [PubMed]

S. J. Chiu, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, and S. Farsiu, “Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images,” Invest. Ophthalmol. Vis. Sci. 53(1), 53–61 (2012).
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T. A. Ciulla, A. G. Amador, and B. Zinman, “Diabetic retinopathy and diabetic macular edema: pathophysiology, screening, and novel therapies,” Diabetes Care 26(9), 2653–2664 (2003).
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J. Yosinki, J. Clune, Y. Bengio, and H. Lipson, “How transferable are features in deep neural networks?” Adv. Neural Inf. Process. Syst. 2014, 3320–3328 (2014).

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A. Coates, H. Lee, and A. Y. Ng, “An analysis of single-layer networks in unsupervised feature learning,” in Proceedings of the 14th International Conference on Artificial Intelligence and Statistics (2011).

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Y. Zhang, B. Zhang, F. Coenen, J. Xiao, and W. Lu, “One-class kernel subspace ensemble for medical image classification,” EURASIP J. Adv. Signal Process. 2014, 1–13 (2014).

Comer, G. M.

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Y. Bengio, A. Courville, and P. Vincent, “Representation learning: A review and new perspectives,” IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013).
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K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. Image Process. 16(8), 2080–2095 (2007).
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Dauphin, Y.

G. Mesnil, Y. Dauphin, X. Glorot, S. Rifai, Y. Bengio, I. J. Goodfellow, E. Lavoie, X. Muller, G. Desjardins, D. Warde-Farley, and P. Vincent, “Unsupervised and transfer learning challenge: A deep learning approach,” ICML Unsupervised and Transfer Learning 27, 97–110 (2012).

De Dzanet, S.

P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schröder, S. De Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
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E. Tátrai, S. Ranganathan, M. Ferencz, D. C. DeBuc, and G. M. Somfai, “Comparison of retinal thickness by Fourier-domain optical coherence tomography and OCT retinal image analysis software segmentation analysis derived from Stratus optical coherence tomography images,” J. Biomed. Opt. 16(5), 056004 (2011).
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Desjardins, G.

G. Mesnil, Y. Dauphin, X. Glorot, S. Rifai, Y. Bengio, I. J. Goodfellow, E. Lavoie, X. Muller, G. Desjardins, D. Warde-Farley, and P. Vincent, “Unsupervised and transfer learning challenge: A deep learning approach,” ICML Unsupervised and Transfer Learning 27, 97–110 (2012).

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J. Duchi, E. Hazan, and Y. Singer, “Adaptive subgradient methods for online learning and stochastic optimization,” J. Mach. Learn. Res. 12, 2121–2159 (2011).

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P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schröder, S. De Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
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K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. Image Process. 16(8), 2080–2095 (2007).
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Escano, M. F.

A. Kanamori, M. Nakamura, M. F. Escano, R. Seya, H. Maeda, and A. Negi, “Evaluation of the glaucomatous damage on retinal nerve fiber layer thickness measured by optical coherence tomography,” Am. J. Ophthalmol. 135(4), 513–520 (2003).
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S. J. Chiu, M. J. Allingham, P. S. Mettu, S. W. Cousins, J. A. Izatt, and S. Farsiu, “Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema,” Biomed. Opt. Express 6(4), 1172–1194 (2015).
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P. P. Srinivasan, L. A. Kim, P. S. Mettu, S. W. Cousins, G. M. Comer, J. A. Izatt, and S. Farsiu, “Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images,” Biomed. Opt. Express 5(10), 3568–3577 (2014).
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P. P. Srinivasan, S. J. Heflin, J. A. Izatt, V. Y. Arshavsky, and S. Farsiu, “Automatic segmentation of up to ten layer boundaries in SD-OCT images of the mouse retina with and without missing layers due to pathology,” Biomed. Opt. Express 5(2), 348–365 (2014).
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S. Farsiu, S. J. Chiu, R. V. O’Connell, F. A. Folgar, E. Yuan, J. A. Izatt, C. A. Toth, and Age-Related Eye Disease Study 2 Ancillary Spectral Domain Optical Coherence Tomography Study Group, “Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography,” Ophthalmology 121(1), 162–172 (2014).
[Crossref] [PubMed]

C. B. Rickman, S. Farsiu, C. A. Toth, and M. Klingeborn, “Dry age-related macular degeneration: Mechanisms, therapeutic targets, and imaging dry AMD mechanisms, targets, and imaging,” Investigative Ophthalmol. Vis. Sci. 54, ORSF68 (2013).

S. J. Chiu, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, and S. Farsiu, “Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images,” Invest. Ophthalmol. Vis. Sci. 53(1), 53–61 (2012).
[Crossref] [PubMed]

Ferencz, M.

E. Tátrai, S. Ranganathan, M. Ferencz, D. C. DeBuc, and G. M. Somfai, “Comparison of retinal thickness by Fourier-domain optical coherence tomography and OCT retinal image analysis software segmentation analysis derived from Stratus optical coherence tomography images,” J. Biomed. Opt. 16(5), 056004 (2011).
[Crossref] [PubMed]

Feuer, W. J.

J.-C. Mwanza, J. D. Oakley, D. L. Budenz, R. T. Chang, O. J. Knight, and W. J. Feuer, “Macular ganglion cell-inner plexiform layer: automated detection and thickness reproducibility with spectral domain-optical coherence tomography in glaucoma,” Invest. Ophthalmol. Vis. Sci. 52(11), 8323–8329 (2011).
[Crossref] [PubMed]

G. Gregori, F. Wang, P. J. Rosenfeld, Z. Yehoshua, N. Z. Gregori, B. J. Lujan, C. A. Puliafito, and W. J. Feuer, “Spectral domain optical coherence tomography imaging of drusen in nonexudative age-related macular degeneration,” Ophthalmology 118(7), 1373–1379 (2011).
[PubMed]

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R. R. Bourne, J. B. Jonas, S. R. Flaxman, J. Keeffe, J. Leasher, K. Naidoo, M. B. Parodi, K. Pesudovs, H. Price, R. A. White, T. Y. Wong, S. Resnikoff, H. R. Taylor, and Vision Loss Expert Group of the Global Burden of Disease Study, “Prevalence and causes of vision loss in high-income countries and in Eastern and Central Europe: 1990-2010,” Br. J. Ophthalmol. 98(5), 629–638 (2014).
[Crossref] [PubMed]

Foi, A.

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. Image Process. 16(8), 2080–2095 (2007).
[Crossref] [PubMed]

Folgar, F. A.

S. Farsiu, S. J. Chiu, R. V. O’Connell, F. A. Folgar, E. Yuan, J. A. Izatt, C. A. Toth, and Age-Related Eye Disease Study 2 Ancillary Spectral Domain Optical Coherence Tomography Study Group, “Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography,” Ophthalmology 121(1), 162–172 (2014).
[Crossref] [PubMed]

Fujimoto, J. G.

W. Drexler and J. G. Fujimoto, “State-of-the-art retinal optical coherence tomography,” Prog. Retin. Eye Res. 27(1), 45–88 (2008).
[Crossref] [PubMed]

Glorot, X.

G. Mesnil, Y. Dauphin, X. Glorot, S. Rifai, Y. Bengio, I. J. Goodfellow, E. Lavoie, X. Muller, G. Desjardins, D. Warde-Farley, and P. Vincent, “Unsupervised and transfer learning challenge: A deep learning approach,” ICML Unsupervised and Transfer Learning 27, 97–110 (2012).

X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feedforward neural networks,” Proc. AISTATS9,249–256 (2010).

Goodfellow, I. J.

G. Mesnil, Y. Dauphin, X. Glorot, S. Rifai, Y. Bengio, I. J. Goodfellow, E. Lavoie, X. Muller, G. Desjardins, D. Warde-Farley, and P. Vincent, “Unsupervised and transfer learning challenge: A deep learning approach,” ICML Unsupervised and Transfer Learning 27, 97–110 (2012).

Gregori, G.

G. Gregori, F. Wang, P. J. Rosenfeld, Z. Yehoshua, N. Z. Gregori, B. J. Lujan, C. A. Puliafito, and W. J. Feuer, “Spectral domain optical coherence tomography imaging of drusen in nonexudative age-related macular degeneration,” Ophthalmology 118(7), 1373–1379 (2011).
[PubMed]

Gregori, N. Z.

G. Gregori, F. Wang, P. J. Rosenfeld, Z. Yehoshua, N. Z. Gregori, B. J. Lujan, C. A. Puliafito, and W. J. Feuer, “Spectral domain optical coherence tomography imaging of drusen in nonexudative age-related macular degeneration,” Ophthalmology 118(7), 1373–1379 (2011).
[PubMed]

Haffner, P.

Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE 86(11), 2278–2324 (1998).
[Crossref]

Hauser, M.

Hazan, E.

J. Duchi, E. Hazan, and Y. Singer, “Adaptive subgradient methods for online learning and stochastic optimization,” J. Mach. Learn. Res. 12, 2121–2159 (2011).

Heflin, S. J.

Hinton, G.

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521(7553), 436–444 (2015).
[Crossref] [PubMed]

Hinton, G. E.

N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. 15(1), 1929–1958 (2014).

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Adv. Neural Inf. Process. Syst. 2012, 1097–1105 (2012).

Hornegger, J.

Izatt, J. A.

S. J. Chiu, M. J. Allingham, P. S. Mettu, S. W. Cousins, J. A. Izatt, and S. Farsiu, “Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema,” Biomed. Opt. Express 6(4), 1172–1194 (2015).
[Crossref] [PubMed]

P. P. Srinivasan, L. A. Kim, P. S. Mettu, S. W. Cousins, G. M. Comer, J. A. Izatt, and S. Farsiu, “Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images,” Biomed. Opt. Express 5(10), 3568–3577 (2014).
[Crossref] [PubMed]

P. P. Srinivasan, S. J. Heflin, J. A. Izatt, V. Y. Arshavsky, and S. Farsiu, “Automatic segmentation of up to ten layer boundaries in SD-OCT images of the mouse retina with and without missing layers due to pathology,” Biomed. Opt. Express 5(2), 348–365 (2014).
[Crossref] [PubMed]

S. Farsiu, S. J. Chiu, R. V. O’Connell, F. A. Folgar, E. Yuan, J. A. Izatt, C. A. Toth, and Age-Related Eye Disease Study 2 Ancillary Spectral Domain Optical Coherence Tomography Study Group, “Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography,” Ophthalmology 121(1), 162–172 (2014).
[Crossref] [PubMed]

S. J. Chiu, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, and S. Farsiu, “Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images,” Invest. Ophthalmol. Vis. Sci. 53(1), 53–61 (2012).
[Crossref] [PubMed]

Jia, W.

S. Ding, H. Zhu, W. Jia, and C. Su, “A survey on feature extraction for pattern recognition,” Artif. Intell. Rev. 37(3), 169–180 (2012).
[Crossref]

Jia, Y.

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 1–9.

Jonas, J. B.

R. R. Bourne, J. B. Jonas, S. R. Flaxman, J. Keeffe, J. Leasher, K. Naidoo, M. B. Parodi, K. Pesudovs, H. Price, R. A. White, T. Y. Wong, S. Resnikoff, H. R. Taylor, and Vision Loss Expert Group of the Global Burden of Disease Study, “Prevalence and causes of vision loss in high-income countries and in Eastern and Central Europe: 1990-2010,” Br. J. Ophthalmol. 98(5), 629–638 (2014).
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R. Kafieh, H. Rabbani, M. D. Abramoff, and M. Sonka, “Intra-retinal layer segmentation of 3D optical coherence tomography using coarse grained diffusion map,” Med. Image Anal. 17(8), 907–928 (2013).
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A. Kanamori, M. Nakamura, M. F. Escano, R. Seya, H. Maeda, and A. Negi, “Evaluation of the glaucomatous damage on retinal nerve fiber layer thickness measured by optical coherence tomography,” Am. J. Ophthalmol. 135(4), 513–520 (2003).
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Karri, S. P. K.

Katkovnik, V.

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. Image Process. 16(8), 2080–2095 (2007).
[Crossref] [PubMed]

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R. R. Bourne, J. B. Jonas, S. R. Flaxman, J. Keeffe, J. Leasher, K. Naidoo, M. B. Parodi, K. Pesudovs, H. Price, R. A. White, T. Y. Wong, S. Resnikoff, H. R. Taylor, and Vision Loss Expert Group of the Global Burden of Disease Study, “Prevalence and causes of vision loss in high-income countries and in Eastern and Central Europe: 1990-2010,” Br. J. Ophthalmol. 98(5), 629–638 (2014).
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Kim, L. A.

Kishi, S.

T. Otani, S. Kishi, and Y. Maruyama, “Patterns of diabetic macular edema with optical coherence tomography,” Am. J. Ophthalmol. 127(6), 688–693 (1999).
[Crossref] [PubMed]

Klingeborn, M.

C. B. Rickman, S. Farsiu, C. A. Toth, and M. Klingeborn, “Dry age-related macular degeneration: Mechanisms, therapeutic targets, and imaging dry AMD mechanisms, targets, and imaging,” Investigative Ophthalmol. Vis. Sci. 54, ORSF68 (2013).

Knight, O. J.

J.-C. Mwanza, J. D. Oakley, D. L. Budenz, R. T. Chang, O. J. Knight, and W. J. Feuer, “Macular ganglion cell-inner plexiform layer: automated detection and thickness reproducibility with spectral domain-optical coherence tomography in glaucoma,” Invest. Ophthalmol. Vis. Sci. 52(11), 8323–8329 (2011).
[Crossref] [PubMed]

Kowal, J.

P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schröder, S. De Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
[Crossref] [PubMed]

Krizhevsky, A.

N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. 15(1), 1929–1958 (2014).

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Adv. Neural Inf. Process. Syst. 2012, 1097–1105 (2012).

Lamoureux, E.

G. Lemaître, M. Rastgoo, J. Massich, C. Y. Cheung, T. Y. Wong, E. Lamoureux, D. Milea, F. Mériaudeau, and D. Sidibé, “Classification of SD-OCT volumes using local binary patterns: Experimental Validation for DME detection,” J. Ophthalmol. 2016, 3298606 (2016).
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G. Mesnil, Y. Dauphin, X. Glorot, S. Rifai, Y. Bengio, I. J. Goodfellow, E. Lavoie, X. Muller, G. Desjardins, D. Warde-Farley, and P. Vincent, “Unsupervised and transfer learning challenge: A deep learning approach,” ICML Unsupervised and Transfer Learning 27, 97–110 (2012).

Leasher, J.

R. R. Bourne, J. B. Jonas, S. R. Flaxman, J. Keeffe, J. Leasher, K. Naidoo, M. B. Parodi, K. Pesudovs, H. Price, R. A. White, T. Y. Wong, S. Resnikoff, H. R. Taylor, and Vision Loss Expert Group of the Global Burden of Disease Study, “Prevalence and causes of vision loss in high-income countries and in Eastern and Central Europe: 1990-2010,” Br. J. Ophthalmol. 98(5), 629–638 (2014).
[Crossref] [PubMed]

LeCun, Y.

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521(7553), 436–444 (2015).
[Crossref] [PubMed]

Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE 86(11), 2278–2324 (1998).
[Crossref]

Lee, H.

A. Coates, H. Lee, and A. Y. Ng, “An analysis of single-layer networks in unsupervised feature learning,” in Proceedings of the 14th International Conference on Artificial Intelligence and Statistics (2011).

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G. Lemaître, M. Rastgoo, J. Massich, C. Y. Cheung, T. Y. Wong, E. Lamoureux, D. Milea, F. Mériaudeau, and D. Sidibé, “Classification of SD-OCT volumes using local binary patterns: Experimental Validation for DME detection,” J. Ophthalmol. 2016, 3298606 (2016).
[Crossref] [PubMed]

Lipson, H.

J. Yosinki, J. Clune, Y. Bengio, and H. Lipson, “How transferable are features in deep neural networks?” Adv. Neural Inf. Process. Syst. 2014, 3320–3328 (2014).

Liu, W.

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 1–9.

Lu, W.

Y. Zhang, B. Zhang, F. Coenen, J. Xiao, and W. Lu, “One-class kernel subspace ensemble for medical image classification,” EURASIP J. Adv. Signal Process. 2014, 1–13 (2014).

Lujan, B. J.

G. Gregori, F. Wang, P. J. Rosenfeld, Z. Yehoshua, N. Z. Gregori, B. J. Lujan, C. A. Puliafito, and W. J. Feuer, “Spectral domain optical coherence tomography imaging of drusen in nonexudative age-related macular degeneration,” Ophthalmology 118(7), 1373–1379 (2011).
[PubMed]

Maeda, H.

A. Kanamori, M. Nakamura, M. F. Escano, R. Seya, H. Maeda, and A. Negi, “Evaluation of the glaucomatous damage on retinal nerve fiber layer thickness measured by optical coherence tomography,” Am. J. Ophthalmol. 135(4), 513–520 (2003).
[Crossref] [PubMed]

Mardin, C. Y.

Maruyama, Y.

T. Otani, S. Kishi, and Y. Maruyama, “Patterns of diabetic macular edema with optical coherence tomography,” Am. J. Ophthalmol. 127(6), 688–693 (1999).
[Crossref] [PubMed]

Massich, J.

G. Lemaître, M. Rastgoo, J. Massich, C. Y. Cheung, T. Y. Wong, E. Lamoureux, D. Milea, F. Mériaudeau, and D. Sidibé, “Classification of SD-OCT volumes using local binary patterns: Experimental Validation for DME detection,” J. Ophthalmol. 2016, 3298606 (2016).
[Crossref] [PubMed]

Mayer, M. A.

Mériaudeau, F.

G. Lemaître, M. Rastgoo, J. Massich, C. Y. Cheung, T. Y. Wong, E. Lamoureux, D. Milea, F. Mériaudeau, and D. Sidibé, “Classification of SD-OCT volumes using local binary patterns: Experimental Validation for DME detection,” J. Ophthalmol. 2016, 3298606 (2016).
[Crossref] [PubMed]

Mesnil, G.

G. Mesnil, Y. Dauphin, X. Glorot, S. Rifai, Y. Bengio, I. J. Goodfellow, E. Lavoie, X. Muller, G. Desjardins, D. Warde-Farley, and P. Vincent, “Unsupervised and transfer learning challenge: A deep learning approach,” ICML Unsupervised and Transfer Learning 27, 97–110 (2012).

Mettu, P. S.

Milea, D.

G. Lemaître, M. Rastgoo, J. Massich, C. Y. Cheung, T. Y. Wong, E. Lamoureux, D. Milea, F. Mériaudeau, and D. Sidibé, “Classification of SD-OCT volumes using local binary patterns: Experimental Validation for DME detection,” J. Ophthalmol. 2016, 3298606 (2016).
[Crossref] [PubMed]

Muller, X.

G. Mesnil, Y. Dauphin, X. Glorot, S. Rifai, Y. Bengio, I. J. Goodfellow, E. Lavoie, X. Muller, G. Desjardins, D. Warde-Farley, and P. Vincent, “Unsupervised and transfer learning challenge: A deep learning approach,” ICML Unsupervised and Transfer Learning 27, 97–110 (2012).

Mwanza, J.-C.

J.-C. Mwanza, J. D. Oakley, D. L. Budenz, R. T. Chang, O. J. Knight, and W. J. Feuer, “Macular ganglion cell-inner plexiform layer: automated detection and thickness reproducibility with spectral domain-optical coherence tomography in glaucoma,” Invest. Ophthalmol. Vis. Sci. 52(11), 8323–8329 (2011).
[Crossref] [PubMed]

Naidoo, K.

R. R. Bourne, J. B. Jonas, S. R. Flaxman, J. Keeffe, J. Leasher, K. Naidoo, M. B. Parodi, K. Pesudovs, H. Price, R. A. White, T. Y. Wong, S. Resnikoff, H. R. Taylor, and Vision Loss Expert Group of the Global Burden of Disease Study, “Prevalence and causes of vision loss in high-income countries and in Eastern and Central Europe: 1990-2010,” Br. J. Ophthalmol. 98(5), 629–638 (2014).
[Crossref] [PubMed]

Nakamura, M.

A. Kanamori, M. Nakamura, M. F. Escano, R. Seya, H. Maeda, and A. Negi, “Evaluation of the glaucomatous damage on retinal nerve fiber layer thickness measured by optical coherence tomography,” Am. J. Ophthalmol. 135(4), 513–520 (2003).
[Crossref] [PubMed]

Negi, A.

A. Kanamori, M. Nakamura, M. F. Escano, R. Seya, H. Maeda, and A. Negi, “Evaluation of the glaucomatous damage on retinal nerve fiber layer thickness measured by optical coherence tomography,” Am. J. Ophthalmol. 135(4), 513–520 (2003).
[Crossref] [PubMed]

Ng, A. Y.

A. Coates, H. Lee, and A. Y. Ng, “An analysis of single-layer networks in unsupervised feature learning,” in Proceedings of the 14th International Conference on Artificial Intelligence and Statistics (2011).

O’Connell, R. V.

S. Farsiu, S. J. Chiu, R. V. O’Connell, F. A. Folgar, E. Yuan, J. A. Izatt, C. A. Toth, and Age-Related Eye Disease Study 2 Ancillary Spectral Domain Optical Coherence Tomography Study Group, “Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography,” Ophthalmology 121(1), 162–172 (2014).
[Crossref] [PubMed]

S. J. Chiu, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, and S. Farsiu, “Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images,” Invest. Ophthalmol. Vis. Sci. 53(1), 53–61 (2012).
[Crossref] [PubMed]

Oakley, J. D.

J.-C. Mwanza, J. D. Oakley, D. L. Budenz, R. T. Chang, O. J. Knight, and W. J. Feuer, “Macular ganglion cell-inner plexiform layer: automated detection and thickness reproducibility with spectral domain-optical coherence tomography in glaucoma,” Invest. Ophthalmol. Vis. Sci. 52(11), 8323–8329 (2011).
[Crossref] [PubMed]

Oquab, M.

M. Oquab, L. Bottou, I. Laptev, and J. Sivic, “Weakly supervised object recognition with convolutional neural networks,” Adv. Neural Inf. Process. Syst. 2014, 1717–1724 (2014).

Otani, T.

T. Otani, S. Kishi, and Y. Maruyama, “Patterns of diabetic macular edema with optical coherence tomography,” Am. J. Ophthalmol. 127(6), 688–693 (1999).
[Crossref] [PubMed]

Pan, S. J.

S. J. Pan and Q. Yang, “A survey on transfer learning,” IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010).
[Crossref]

Parodi, M. B.

R. R. Bourne, J. B. Jonas, S. R. Flaxman, J. Keeffe, J. Leasher, K. Naidoo, M. B. Parodi, K. Pesudovs, H. Price, R. A. White, T. Y. Wong, S. Resnikoff, H. R. Taylor, and Vision Loss Expert Group of the Global Burden of Disease Study, “Prevalence and causes of vision loss in high-income countries and in Eastern and Central Europe: 1990-2010,” Br. J. Ophthalmol. 98(5), 629–638 (2014).
[Crossref] [PubMed]

Pesudovs, K.

R. R. Bourne, J. B. Jonas, S. R. Flaxman, J. Keeffe, J. Leasher, K. Naidoo, M. B. Parodi, K. Pesudovs, H. Price, R. A. White, T. Y. Wong, S. Resnikoff, H. R. Taylor, and Vision Loss Expert Group of the Global Burden of Disease Study, “Prevalence and causes of vision loss in high-income countries and in Eastern and Central Europe: 1990-2010,” Br. J. Ophthalmol. 98(5), 629–638 (2014).
[Crossref] [PubMed]

Platt, J. C.

P. Y. Simard, D. Steinkraus, and J. C. Platt, “Best practices for convolutional neural networks applied to visual document analysis,” In International Conference on Document Analysis, 3, 958–963 (2003).
[Crossref]

Price, H.

R. R. Bourne, J. B. Jonas, S. R. Flaxman, J. Keeffe, J. Leasher, K. Naidoo, M. B. Parodi, K. Pesudovs, H. Price, R. A. White, T. Y. Wong, S. Resnikoff, H. R. Taylor, and Vision Loss Expert Group of the Global Burden of Disease Study, “Prevalence and causes of vision loss in high-income countries and in Eastern and Central Europe: 1990-2010,” Br. J. Ophthalmol. 98(5), 629–638 (2014).
[Crossref] [PubMed]

Prince, J. L.

Puliafito, C. A.

G. Gregori, F. Wang, P. J. Rosenfeld, Z. Yehoshua, N. Z. Gregori, B. J. Lujan, C. A. Puliafito, and W. J. Feuer, “Spectral domain optical coherence tomography imaging of drusen in nonexudative age-related macular degeneration,” Ophthalmology 118(7), 1373–1379 (2011).
[PubMed]

Rabbani, H.

R. Kafieh, H. Rabbani, M. D. Abramoff, and M. Sonka, “Intra-retinal layer segmentation of 3D optical coherence tomography using coarse grained diffusion map,” Med. Image Anal. 17(8), 907–928 (2013).
[Crossref] [PubMed]

Rabinovich, A.

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 1–9.

Ranganathan, S.

E. Tátrai, S. Ranganathan, M. Ferencz, D. C. DeBuc, and G. M. Somfai, “Comparison of retinal thickness by Fourier-domain optical coherence tomography and OCT retinal image analysis software segmentation analysis derived from Stratus optical coherence tomography images,” J. Biomed. Opt. 16(5), 056004 (2011).
[Crossref] [PubMed]

Rastgoo, M.

G. Lemaître, M. Rastgoo, J. Massich, C. Y. Cheung, T. Y. Wong, E. Lamoureux, D. Milea, F. Mériaudeau, and D. Sidibé, “Classification of SD-OCT volumes using local binary patterns: Experimental Validation for DME detection,” J. Ophthalmol. 2016, 3298606 (2016).
[Crossref] [PubMed]

Rathke, F.

F. Rathke, S. Schmidt, and C. Schnörr, “Probabilistic intra-retinal layer segmentation in 3-D OCT images using global shape regularization,” Med. Image Anal. 18(5), 781–794 (2014).
[Crossref] [PubMed]

Reed, S.

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 1–9.

Resnikoff, S.

R. R. Bourne, J. B. Jonas, S. R. Flaxman, J. Keeffe, J. Leasher, K. Naidoo, M. B. Parodi, K. Pesudovs, H. Price, R. A. White, T. Y. Wong, S. Resnikoff, H. R. Taylor, and Vision Loss Expert Group of the Global Burden of Disease Study, “Prevalence and causes of vision loss in high-income countries and in Eastern and Central Europe: 1990-2010,” Br. J. Ophthalmol. 98(5), 629–638 (2014).
[Crossref] [PubMed]

Rickman, C. B.

C. B. Rickman, S. Farsiu, C. A. Toth, and M. Klingeborn, “Dry age-related macular degeneration: Mechanisms, therapeutic targets, and imaging dry AMD mechanisms, targets, and imaging,” Investigative Ophthalmol. Vis. Sci. 54, ORSF68 (2013).

Rifai, S.

G. Mesnil, Y. Dauphin, X. Glorot, S. Rifai, Y. Bengio, I. J. Goodfellow, E. Lavoie, X. Muller, G. Desjardins, D. Warde-Farley, and P. Vincent, “Unsupervised and transfer learning challenge: A deep learning approach,” ICML Unsupervised and Transfer Learning 27, 97–110 (2012).

Romero-Aroca, P.

P. Romero-Aroca, “Current status in diabetic macular edema treatments,” World J. Diabetes 4(5), 165–169 (2013).
[PubMed]

Rosenfeld, P. J.

G. Gregori, F. Wang, P. J. Rosenfeld, Z. Yehoshua, N. Z. Gregori, B. J. Lujan, C. A. Puliafito, and W. J. Feuer, “Spectral domain optical coherence tomography imaging of drusen in nonexudative age-related macular degeneration,” Ophthalmology 118(7), 1373–1379 (2011).
[PubMed]

Salakhutdinov, R.

N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. 15(1), 1929–1958 (2014).

Schmidt, S.

F. Rathke, S. Schmidt, and C. Schnörr, “Probabilistic intra-retinal layer segmentation in 3-D OCT images using global shape regularization,” Med. Image Anal. 18(5), 781–794 (2014).
[Crossref] [PubMed]

Schnörr, C.

F. Rathke, S. Schmidt, and C. Schnörr, “Probabilistic intra-retinal layer segmentation in 3-D OCT images using global shape regularization,” Med. Image Anal. 18(5), 781–794 (2014).
[Crossref] [PubMed]

Schröder, S.

P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schröder, S. De Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
[Crossref] [PubMed]

Sermanet, P.

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 1–9.

Seya, R.

A. Kanamori, M. Nakamura, M. F. Escano, R. Seya, H. Maeda, and A. Negi, “Evaluation of the glaucomatous damage on retinal nerve fiber layer thickness measured by optical coherence tomography,” Am. J. Ophthalmol. 135(4), 513–520 (2003).
[Crossref] [PubMed]

Sidibé, D.

G. Lemaître, M. Rastgoo, J. Massich, C. Y. Cheung, T. Y. Wong, E. Lamoureux, D. Milea, F. Mériaudeau, and D. Sidibé, “Classification of SD-OCT volumes using local binary patterns: Experimental Validation for DME detection,” J. Ophthalmol. 2016, 3298606 (2016).
[Crossref] [PubMed]

Simard, P. Y.

P. Y. Simard, D. Steinkraus, and J. C. Platt, “Best practices for convolutional neural networks applied to visual document analysis,” In International Conference on Document Analysis, 3, 958–963 (2003).
[Crossref]

Singer, Y.

J. Duchi, E. Hazan, and Y. Singer, “Adaptive subgradient methods for online learning and stochastic optimization,” J. Mach. Learn. Res. 12, 2121–2159 (2011).

Sivic, J.

M. Oquab, L. Bottou, I. Laptev, and J. Sivic, “Weakly supervised object recognition with convolutional neural networks,” Adv. Neural Inf. Process. Syst. 2014, 1717–1724 (2014).

Somfai, G. M.

E. Tátrai, S. Ranganathan, M. Ferencz, D. C. DeBuc, and G. M. Somfai, “Comparison of retinal thickness by Fourier-domain optical coherence tomography and OCT retinal image analysis software segmentation analysis derived from Stratus optical coherence tomography images,” J. Biomed. Opt. 16(5), 056004 (2011).
[Crossref] [PubMed]

Sonka, M.

R. Kafieh, H. Rabbani, M. D. Abramoff, and M. Sonka, “Intra-retinal layer segmentation of 3D optical coherence tomography using coarse grained diffusion map,” Med. Image Anal. 17(8), 907–928 (2013).
[Crossref] [PubMed]

Srinivasan, P. P.

Srivastava, N.

N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. 15(1), 1929–1958 (2014).

Steinkraus, D.

P. Y. Simard, D. Steinkraus, and J. C. Platt, “Best practices for convolutional neural networks applied to visual document analysis,” In International Conference on Document Analysis, 3, 958–963 (2003).
[Crossref]

Su, C.

S. Ding, H. Zhu, W. Jia, and C. Su, “A survey on feature extraction for pattern recognition,” Artif. Intell. Rev. 37(3), 169–180 (2012).
[Crossref]

Sutskever, I.

N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. 15(1), 1929–1958 (2014).

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Adv. Neural Inf. Process. Syst. 2012, 1097–1105 (2012).

Szegedy, C.

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 1–9.

Tátrai, E.

E. Tátrai, S. Ranganathan, M. Ferencz, D. C. DeBuc, and G. M. Somfai, “Comparison of retinal thickness by Fourier-domain optical coherence tomography and OCT retinal image analysis software segmentation analysis derived from Stratus optical coherence tomography images,” J. Biomed. Opt. 16(5), 056004 (2011).
[Crossref] [PubMed]

Taylor, H. R.

R. R. Bourne, J. B. Jonas, S. R. Flaxman, J. Keeffe, J. Leasher, K. Naidoo, M. B. Parodi, K. Pesudovs, H. Price, R. A. White, T. Y. Wong, S. Resnikoff, H. R. Taylor, and Vision Loss Expert Group of the Global Burden of Disease Study, “Prevalence and causes of vision loss in high-income countries and in Eastern and Central Europe: 1990-2010,” Br. J. Ophthalmol. 98(5), 629–638 (2014).
[Crossref] [PubMed]

Tornow, R. P.

Toth, C. A.

S. Farsiu, S. J. Chiu, R. V. O’Connell, F. A. Folgar, E. Yuan, J. A. Izatt, C. A. Toth, and Age-Related Eye Disease Study 2 Ancillary Spectral Domain Optical Coherence Tomography Study Group, “Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography,” Ophthalmology 121(1), 162–172 (2014).
[Crossref] [PubMed]

C. B. Rickman, S. Farsiu, C. A. Toth, and M. Klingeborn, “Dry age-related macular degeneration: Mechanisms, therapeutic targets, and imaging dry AMD mechanisms, targets, and imaging,” Investigative Ophthalmol. Vis. Sci. 54, ORSF68 (2013).

S. J. Chiu, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, and S. Farsiu, “Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images,” Invest. Ophthalmol. Vis. Sci. 53(1), 53–61 (2012).
[Crossref] [PubMed]

Vanhoucke, V.

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 1–9.

Vincent, P.

Y. Bengio, A. Courville, and P. Vincent, “Representation learning: A review and new perspectives,” IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013).
[Crossref] [PubMed]

G. Mesnil, Y. Dauphin, X. Glorot, S. Rifai, Y. Bengio, I. J. Goodfellow, E. Lavoie, X. Muller, G. Desjardins, D. Warde-Farley, and P. Vincent, “Unsupervised and transfer learning challenge: A deep learning approach,” ICML Unsupervised and Transfer Learning 27, 97–110 (2012).

Wagner, M.

Wang, F.

G. Gregori, F. Wang, P. J. Rosenfeld, Z. Yehoshua, N. Z. Gregori, B. J. Lujan, C. A. Puliafito, and W. J. Feuer, “Spectral domain optical coherence tomography imaging of drusen in nonexudative age-related macular degeneration,” Ophthalmology 118(7), 1373–1379 (2011).
[PubMed]

Wang, Y.

Warde-Farley, D.

G. Mesnil, Y. Dauphin, X. Glorot, S. Rifai, Y. Bengio, I. J. Goodfellow, E. Lavoie, X. Muller, G. Desjardins, D. Warde-Farley, and P. Vincent, “Unsupervised and transfer learning challenge: A deep learning approach,” ICML Unsupervised and Transfer Learning 27, 97–110 (2012).

White, R. A.

R. R. Bourne, J. B. Jonas, S. R. Flaxman, J. Keeffe, J. Leasher, K. Naidoo, M. B. Parodi, K. Pesudovs, H. Price, R. A. White, T. Y. Wong, S. Resnikoff, H. R. Taylor, and Vision Loss Expert Group of the Global Burden of Disease Study, “Prevalence and causes of vision loss in high-income countries and in Eastern and Central Europe: 1990-2010,” Br. J. Ophthalmol. 98(5), 629–638 (2014).
[Crossref] [PubMed]

Winter, K. P.

S. J. Chiu, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, and S. Farsiu, “Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images,” Invest. Ophthalmol. Vis. Sci. 53(1), 53–61 (2012).
[Crossref] [PubMed]

Wolf-Schnurrbusch, U.

P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schröder, S. De Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
[Crossref] [PubMed]

Wong, T. Y.

G. Lemaître, M. Rastgoo, J. Massich, C. Y. Cheung, T. Y. Wong, E. Lamoureux, D. Milea, F. Mériaudeau, and D. Sidibé, “Classification of SD-OCT volumes using local binary patterns: Experimental Validation for DME detection,” J. Ophthalmol. 2016, 3298606 (2016).
[Crossref] [PubMed]

R. R. Bourne, J. B. Jonas, S. R. Flaxman, J. Keeffe, J. Leasher, K. Naidoo, M. B. Parodi, K. Pesudovs, H. Price, R. A. White, T. Y. Wong, S. Resnikoff, H. R. Taylor, and Vision Loss Expert Group of the Global Burden of Disease Study, “Prevalence and causes of vision loss in high-income countries and in Eastern and Central Europe: 1990-2010,” Br. J. Ophthalmol. 98(5), 629–638 (2014).
[Crossref] [PubMed]

Xiao, J.

Y. Zhang, B. Zhang, F. Coenen, J. Xiao, and W. Lu, “One-class kernel subspace ensemble for medical image classification,” EURASIP J. Adv. Signal Process. 2014, 1–13 (2014).

Yang, Q.

S. J. Pan and Q. Yang, “A survey on transfer learning,” IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010).
[Crossref]

Yao, Z.

Yehoshua, Z.

G. Gregori, F. Wang, P. J. Rosenfeld, Z. Yehoshua, N. Z. Gregori, B. J. Lujan, C. A. Puliafito, and W. J. Feuer, “Spectral domain optical coherence tomography imaging of drusen in nonexudative age-related macular degeneration,” Ophthalmology 118(7), 1373–1379 (2011).
[PubMed]

Ying, H. S.

Yosinki, J.

J. Yosinki, J. Clune, Y. Bengio, and H. Lipson, “How transferable are features in deep neural networks?” Adv. Neural Inf. Process. Syst. 2014, 3320–3328 (2014).

Yuan, E.

S. Farsiu, S. J. Chiu, R. V. O’Connell, F. A. Folgar, E. Yuan, J. A. Izatt, C. A. Toth, and Age-Related Eye Disease Study 2 Ancillary Spectral Domain Optical Coherence Tomography Study Group, “Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography,” Ophthalmology 121(1), 162–172 (2014).
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Zhang, B.

Y. Zhang, B. Zhang, F. Coenen, J. Xiao, and W. Lu, “One-class kernel subspace ensemble for medical image classification,” EURASIP J. Adv. Signal Process. 2014, 1–13 (2014).

Zhang, Y.

Y. Wang, Y. Zhang, Z. Yao, R. Zhao, and F. Zhou, “Machine learning based detection of age-related macular degeneration (AMD) and diabetic macular edema (DME) from optical coherence tomography (OCT) images,” Biomed. Opt. Express 7(12), 4928–4940 (2016).
[Crossref]

Y. Zhang, B. Zhang, F. Coenen, J. Xiao, and W. Lu, “One-class kernel subspace ensemble for medical image classification,” EURASIP J. Adv. Signal Process. 2014, 1–13 (2014).

Zhao, R.

Zhou, F.

Zhu, H.

S. Ding, H. Zhu, W. Jia, and C. Su, “A survey on feature extraction for pattern recognition,” Artif. Intell. Rev. 37(3), 169–180 (2012).
[Crossref]

Zinman, B.

T. A. Ciulla, A. G. Amador, and B. Zinman, “Diabetic retinopathy and diabetic macular edema: pathophysiology, screening, and novel therapies,” Diabetes Care 26(9), 2653–2664 (2003).
[Crossref] [PubMed]

Adv. Neural Inf. Process. Syst. (4)

M. Oquab, L. Bottou, I. Laptev, and J. Sivic, “Weakly supervised object recognition with convolutional neural networks,” Adv. Neural Inf. Process. Syst. 2014, 1717–1724 (2014).

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Adv. Neural Inf. Process. Syst. 2012, 1097–1105 (2012).

Y. L. Boureau and Y. L. Cun, “Sparse feature learning for deep belief networks,” Adv. Neural Inf. Process. Syst. 2008, 1185–1192 (2008).

J. Yosinki, J. Clune, Y. Bengio, and H. Lipson, “How transferable are features in deep neural networks?” Adv. Neural Inf. Process. Syst. 2014, 3320–3328 (2014).

Am. J. Ophthalmol. (2)

A. Kanamori, M. Nakamura, M. F. Escano, R. Seya, H. Maeda, and A. Negi, “Evaluation of the glaucomatous damage on retinal nerve fiber layer thickness measured by optical coherence tomography,” Am. J. Ophthalmol. 135(4), 513–520 (2003).
[Crossref] [PubMed]

T. Otani, S. Kishi, and Y. Maruyama, “Patterns of diabetic macular edema with optical coherence tomography,” Am. J. Ophthalmol. 127(6), 688–693 (1999).
[Crossref] [PubMed]

Artif. Intell. Rev. (1)

S. Ding, H. Zhu, W. Jia, and C. Su, “A survey on feature extraction for pattern recognition,” Artif. Intell. Rev. 37(3), 169–180 (2012).
[Crossref]

Biomed. Opt. Express (7)

P. P. Srinivasan, L. A. Kim, P. S. Mettu, S. W. Cousins, G. M. Comer, J. A. Izatt, and S. Farsiu, “Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images,” Biomed. Opt. Express 5(10), 3568–3577 (2014).
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Y. Wang, Y. Zhang, Z. Yao, R. Zhao, and F. Zhou, “Machine learning based detection of age-related macular degeneration (AMD) and diabetic macular edema (DME) from optical coherence tomography (OCT) images,” Biomed. Opt. Express 7(12), 4928–4940 (2016).
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M. A. Mayer, A. Borsdorf, M. Wagner, J. Hornegger, C. Y. Mardin, and R. P. Tornow, “Wavelet denoising of multiframe optical coherence tomography data,” Biomed. Opt. Express 3(3), 572–589 (2012).
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A. Carass, A. Lang, M. Hauser, P. A. Calabresi, H. S. Ying, and J. L. Prince, “Multiple-object geometric deformable model for segmentation of macular OCT,” Biomed. Opt. Express 5(4), 1062–1074 (2014).
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P. P. Srinivasan, S. J. Heflin, J. A. Izatt, V. Y. Arshavsky, and S. Farsiu, “Automatic segmentation of up to ten layer boundaries in SD-OCT images of the mouse retina with and without missing layers due to pathology,” Biomed. Opt. Express 5(2), 348–365 (2014).
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S. J. Chiu, M. J. Allingham, P. S. Mettu, S. W. Cousins, J. A. Izatt, and S. Farsiu, “Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema,” Biomed. Opt. Express 6(4), 1172–1194 (2015).
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S. P. K. Karri, D. Chakraborthi, and J. Chatterjee, “Learning layer-specific edges for segmenting retinal layers with large deformations,” Biomed. Opt. Express 7(7), 2888–2901 (2016).
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Br. J. Ophthalmol. (1)

R. R. Bourne, J. B. Jonas, S. R. Flaxman, J. Keeffe, J. Leasher, K. Naidoo, M. B. Parodi, K. Pesudovs, H. Price, R. A. White, T. Y. Wong, S. Resnikoff, H. R. Taylor, and Vision Loss Expert Group of the Global Burden of Disease Study, “Prevalence and causes of vision loss in high-income countries and in Eastern and Central Europe: 1990-2010,” Br. J. Ophthalmol. 98(5), 629–638 (2014).
[Crossref] [PubMed]

Diabetes Care (1)

T. A. Ciulla, A. G. Amador, and B. Zinman, “Diabetic retinopathy and diabetic macular edema: pathophysiology, screening, and novel therapies,” Diabetes Care 26(9), 2653–2664 (2003).
[Crossref] [PubMed]

EURASIP J. Adv. Signal Process. (1)

Y. Zhang, B. Zhang, F. Coenen, J. Xiao, and W. Lu, “One-class kernel subspace ensemble for medical image classification,” EURASIP J. Adv. Signal Process. 2014, 1–13 (2014).

ICML Unsupervised and Transfer Learning (1)

G. Mesnil, Y. Dauphin, X. Glorot, S. Rifai, Y. Bengio, I. J. Goodfellow, E. Lavoie, X. Muller, G. Desjardins, D. Warde-Farley, and P. Vincent, “Unsupervised and transfer learning challenge: A deep learning approach,” ICML Unsupervised and Transfer Learning 27, 97–110 (2012).

IEEE Trans. Image Process. (1)

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. Image Process. 16(8), 2080–2095 (2007).
[Crossref] [PubMed]

IEEE Trans. Knowl. Data Eng. (1)

S. J. Pan and Q. Yang, “A survey on transfer learning,” IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010).
[Crossref]

IEEE Trans. Med. Imaging (1)

P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schröder, S. De Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
[Crossref] [PubMed]

IEEE Trans. Pattern Anal. Mach. Intell. (1)

Y. Bengio, A. Courville, and P. Vincent, “Representation learning: A review and new perspectives,” IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013).
[Crossref] [PubMed]

Invest. Ophthalmol. Vis. Sci. (2)

J.-C. Mwanza, J. D. Oakley, D. L. Budenz, R. T. Chang, O. J. Knight, and W. J. Feuer, “Macular ganglion cell-inner plexiform layer: automated detection and thickness reproducibility with spectral domain-optical coherence tomography in glaucoma,” Invest. Ophthalmol. Vis. Sci. 52(11), 8323–8329 (2011).
[Crossref] [PubMed]

S. J. Chiu, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, and S. Farsiu, “Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images,” Invest. Ophthalmol. Vis. Sci. 53(1), 53–61 (2012).
[Crossref] [PubMed]

Investigative Ophthalmol. Vis. Sci. (1)

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Figures (7)

Fig. 1
Fig. 1 Layer deformation due to drusen (geographic atrophy) and fluid in dry AMD and DME in comparison to a normal subject‘s retina (the white regions are introduced when exporting from OCT machines to maintain the image size). (a) Normal. (b) AMD. (c) DME.
Fig. 2
Fig. 2 GoogLeNet with filter dimensions and illustration of inception layer.
Fig. 3
Fig. 3 Illustrating intermediate results of preprocessing. (a) Input image. (b) Saturated pixel removal. (c) Flattening. (d) Resizing. (e) BM3D filtering.
Fig. 4
Fig. 4 Repeatability of experimentation in terms of test accuracy also illustrates test accuracy divergence.
Fig. 5
Fig. 5 Test accuracy of fine-tuned GoogLeNet (transfer) in comparison to random initialization.
Fig. 6
Fig. 6 Identified potential response of conv1, ReLU, Maxpool1, LRN1, Inception7, Maxpool4, Inception8 and Inception9 layers given AMD test image.
Fig. 7
Fig. 7 Identified potential response of conv1, ReLU, Maxpool1, LRN1, Inception7, Maxpool4, Inception8 and Inception9 layers given DME test image.

Tables (1)

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Table 1 Decision pooling evaluation across proposed, random initialization, and benchmark

Metrics