-
1
-
-
77956941136
-
Histopathological image analysis: A review
-
Gurcan MN, Boucheron LE, Can A, Madabhushi A, Rajpoot NM, Yener B. Histopathological image analysis: A review. IEEE Rev Biomed Eng 2009;2:147-71.
-
(2009)
IEEE Rev Biomed Eng
, vol.2
, pp. 147-171
-
-
Gurcan, M.N.1
Boucheron, L.E.2
Can, A.3
Madabhushi, A.4
Rajpoot, N.M.5
Yener, B.6
-
3
-
-
84978977016
-
A review of emerging themes in image informatics and molecular analysis for digital pathology
-
Last accessed on Apr 19
-
Bhargava R, Madabhushi A. A review of emerging themes in image informatics and molecular analysis for digital pathology. Annu Rev Biomed Eng 2016;18. [Last accessed on 2016 Apr 19].
-
(2016)
Annu Rev Biomed Eng
, vol.18
-
-
Bhargava, R.1
Madabhushi, A.2
-
4
-
-
84891634649
-
A quantitative histomorphometric classifier (QuHbIC) identifies aggressive versus indolent p16-positive oropharyngeal squamous cell carcinoma
-
Lewis JS Jr., Ali S, Luo J, Thorstad WL, Madabhushi A. A quantitative histomorphometric classifier (QuHbIC) identifies aggressive versus indolent p16-positive oropharyngeal squamous cell carcinoma. Am J Surg Pathol 2014;38:128-37.
-
(2014)
Am J Surg Pathol
, vol.38
, pp. 128-137
-
-
Lewis, J.S.1
Ali, S.2
Luo, J.3
Thorstad, W.L.4
Madabhushi, A.5
-
5
-
-
84878560048
-
Multi-field-of-view strategy for image-based outcome prediction of multi-parametric estrogen receptor-positive breast cancer histopathology: Comparison to oncotype DX
-
Basavanhally A, Feldman M, Shih N, Mies C, Tomaszewski J, Ganesan S, et al. Multi-field-of-view strategy for image-based outcome prediction of multi-parametric estrogen receptor-positive breast cancer histopathology: Comparison to oncotype DX. J Pathol Inform 2011;2:S1.
-
(2011)
J Pathol Inform
, vol.2
, pp. S1
-
-
Basavanhally, A.1
Feldman, M.2
Shih, N.3
Mies, C.4
Tomaszewski, J.5
Ganesan, S.6
-
6
-
-
84920921065
-
Assessment of algorithms for mitosis detection in breast cancer histopathology images
-
Veta M, van Diest PJ, Willems SM, Wang H, Madabhushi A, Cruz-Roa A, et al. Assessment of algorithms for mitosis detection in breast cancer histopathology images. Med Image Anal 2015;20:237-48.
-
(2015)
Med Image Anal
, vol.20
, pp. 237-248
-
-
Veta, M.1
Van Diest, P.J.2
Willems, S.M.3
Wang, H.4
Madabhushi, A.5
Cruz-Roa, A.6
-
7
-
-
84885922439
-
Mitosis detection in breast cancer histological images An ICPR 2012 contest
-
Roux L, Racoceanu D, Loménie N, Kulikova M, Irshad H, Klossa J, et al. Mitosis detection in breast cancer histological images An ICPR 2012 contest. J Pathol Inform 2013;4:8.
-
(2013)
J Pathol Inform
, vol.4
, pp. 8
-
-
Roux, L.1
Racoceanu, D.2
Loménie, N.3
Kulikova, M.4
Irshad, H.5
Klossa, J.6
-
9
-
-
84901774997
-
Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks
-
Cruz-Roa A, Basavanhally A, González F, Gilmore H, Feldman M, Ganesan S, et al. Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks. In: SPIE Medical Imaging. Vol. 9041. ;2014. p. 904103-904103-15.
-
(2014)
SPIE Medical Imaging
, vol.9041
, pp. 904103-90410315
-
-
Cruz-Roa, A.1
Basavanhally, A.2
González, F.3
Gilmore, H.4
Feldman, M.5
Ganesan, S.6
-
10
-
-
84921737539
-
Deep learning based automatic immune cell detection for immunohistochemistry images
-
In: Wu G, Zhang D, Zhou L, editors..: Springer International Publishing
-
Chen T, Chefd'hotel C. Deep learning based automatic immune cell detection for immunohistochemistry images. In: Wu G, Zhang D, Zhou L, editors. Machine Learning in Medical Imaging. (Lecture Notes in Computer Science).Vol. 8679.: Springer International Publishing; 2014. p. 17-24.
-
(2014)
Machine Learning in Medical Imaging. (Lecture Notes in Computer Science)
, vol.8679
, pp. 17-24
-
-
Chen, T.1
Chefd'Hotel, C.2
-
12
-
-
85009281409
-
-
Deep Learning and Unsupervised Feature Learning NIPS 2012 Workshop
-
Bastien F, Lamblin P, Pascanu R, Bergstra J, Goodfellow IJ, Bergeron A, et al. Theano: New Features and Speed Improvements. Deep Learning and Unsupervised Feature Learning NIPS 2012 Workshop; 2012.
-
(2012)
Theano: New Features and Speed Improvements
-
-
Bastien, F.1
Lamblin, P.2
Pascanu, R.3
Bergstra, J.4
Goodfellow, I.J.5
Bergeron, A.6
-
13
-
-
0001857994
-
Efficient backprop
-
In: Orr G, Müller KR, editors.. Springer
-
LeCun Y, Bottou L, Orr G, Muller K. Efficient backprop. In: Orr G, Müller KR, editors. Neural Networks: Tricks of the Trade. Springer; 1998.
-
(1998)
Neural Networks: Tricks of the Trade
-
-
LeCun, Y.1
Bottou, L.2
Orr, G.3
Muller, K.4
-
15
-
-
84877789057
-
Deep neural networks segment neuronal membranes in electron microscopy images
-
In: Pereira F, Burges C, Bottou L, Weinberger K, editors.. Curran Associates, Inc
-
Ciresan D, Giusti A, Gambardella LM, Schmidhuber J. Deep neural networks segment neuronal membranes in electron microscopy images. In: Pereira F, Burges C, Bottou L, Weinberger K, editors. Advances in Neural Information Processing Systems 25. Curran Associates, Inc.; 2012. p. 2843-51.
-
(2012)
Advances in Neural Information Processing Systems 25
, pp. 2843-2851
-
-
Ciresan, D.1
Giusti, A.2
Gambardella, L.M.3
Schmidhuber, J.4
-
17
-
-
84955260373
-
Automated nuclear pleomorphism scoring in breast cancer histopathology images using deep neural networks
-
In: Prasath R, Vuppala AK, Kathirvalavakumar T, editors.. Springer International Publishing
-
Maqlin P, Thamburaj R, Mammen J, Manipadam M. Automated nuclear pleomorphism scoring in breast cancer histopathology images using deep neural networks. In: Prasath R, Vuppala AK, Kathirvalavakumar T, editors. Mining Intelligence and Knowledge Exploration. (Lecture Notes in Computer Science). Vol. 9468. Springer International Publishing; 2015. p. 269-76.
-
(2015)
Mining Intelligence and Knowledge Exploration. (Lecture Notes in Computer Science)
, vol.9468
, pp. 269-276
-
-
Maqlin, P.1
Thamburaj, R.2
Mammen, J.3
Manipadam, M.4
-
18
-
-
84968542311
-
-
IEEE Trans Med Imaging
-
Sirinukunwattana K, Raza S, Tsang YW, Snead D, Cree I, Rajpoot N. Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans Med Imaging 2016.
-
(2016)
Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images
-
-
Sirinukunwattana, K.1
Raza, S.2
Tsang, Y.W.3
Snead, D.4
Cree, I.5
Rajpoot, N.6
-
19
-
-
84944327102
-
Nuclei segmentation via sparsity constrained convolutional regression
-
April
-
Zhou Y, Chang H, Barner KE, Parvin B. Nuclei Segmentation via Sparsity Constrained Convolutional Regression. In: Biomedical Imaging (ISBI), 2015 IEEE 12th International Symposium on; April, 2015. p. 1284-7.
-
(2015)
Biomedical Imaging (ISBI), 2015 IEEE 12th International Symposium on
, pp. 1284-1287
-
-
Zhou, Y.1
Chang, H.2
Barner, K.E.3
Parvin, B.4
-
20
-
-
84959375736
-
Stacked sparse autoencoder (SSAE) for nuclei detection on breast cancer histopathology images
-
Xu J, Xiang L, Liu Q, Gilmore H, Wu J, Tang J, et al. Stacked sparse autoencoder (SSAE) for nuclei detection on breast cancer histopathology images. IEEE Trans Med Imaging 2016;35:119-30.
-
(2016)
IEEE Trans Med Imaging
, vol.35
, pp. 119-130
-
-
Xu, J.1
Xiang, L.2
Liu, Q.3
Gilmore, H.4
Wu, J.5
Tang, J.6
-
21
-
-
84946045951
-
Deep convolutional activation features for large scale brain tumor histopathology image classification and segmentation
-
April
-
Xu Y, Jia Z, Ai Y, Zhang F, Lai M, Chang EIC. Deep Convolutional Activation Features for Large Scale Brain Tumor Histopathology Image Classification and Segmentation. In: Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on; April, 2015. p. 947-51.
-
(2015)
Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on
, pp. 947-951
-
-
Xu, Y.1
Jia, Z.2
Ai, Y.3
Zhang, F.4
Lai, M.5
Chang, E.I.C.6
-
22
-
-
84955255437
-
A spatially constrained deep learning framework for detection of epithelial tumor nuclei in cancer histology images
-
In: Wu G, Coupé P, Zhan Y, Munsell B, Rueckert D, editors.. (Lecture Notes in Computer Science). Springer International Publishing
-
Sirinukunwattana K, Ahmed Raza S, Tsang Y, Snead D, Cree I, Rajpoot N. A spatially constrained deep learning framework for detection of epithelial tumor nuclei in cancer histology images. In: Wu G, Coupé P, Zhan Y, Munsell B, Rueckert D, editors. Patch-Based Techniques in Medical Imaging. Vol. 9467. (Lecture Notes in Computer Science). Springer International Publishing; 2015. p. 154-62.
-
(2015)
Patch-Based Techniques in Medical Imaging
, vol.9467
, pp. 154-162
-
-
Sirinukunwattana, K.1
Ahmed Raza, S.2
Tsang, Y.3
Snead, D.4
Cree, I.5
Rajpoot, N.6
-
23
-
-
84977845763
-
A deep convolutional neural network for segmenting and classifying epithelial and stromal regions in histopathological images
-
Xu J, Luo X, Wang G, Gilmore H, Madabhushi A. A deep convolutional neural network for segmenting and classifying epithelial and stromal regions in histopathological images. Neurocomputing 2016;191:214-23.
-
(2016)
Neurocomputing
, vol.191
, pp. 214-223
-
-
Xu, J.1
Luo, X.2
Wang, G.3
Gilmore, H.4
Madabhushi, A.5
-
24
-
-
85042106575
-
Automated grading of gliomas using deep learning in digital pathology images: A modular approach with ensemble of convolutional neural networks
-
Ertosun MG, Rubin DL. Automated grading of gliomas using deep learning in digital pathology images: A modular approach with ensemble of convolutional neural networks. AMIA Annu Symp Proc 2015;2015:1899-908.
-
(2015)
AMIA Annu Symp Proc
, vol.2015
, pp. 1899-1908
-
-
Ertosun, M.G.1
Rubin, D.L.2
-
25
-
-
84913555165
-
-
arXiv preprint arXiv: 1408.5093
-
Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, et al. Caffe: Convolutional Architecture for Fast Feature Embedding. arXiv preprint arXiv: 1408.5093; 2014.
-
(2014)
Caffe: Convolutional Architecture for Fast Feature Embedding
-
-
Jia, Y.1
Shelhamer, E.2
Donahue, J.3
Karayev, S.4
Long, J.5
Girshick, R.6
-
27
-
-
84876231242
-
Imagenet classification with deep convolutional neural networks
-
In: Pereira F, Burges C, Bottou L, Weinberger K, editors.. Curran Associates, Inc
-
Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. In: Pereira F, Burges C, Bottou L, Weinberger K, editors. Advances in Neural Information Processing Systems 25. Curran Associates, Inc.; 2012. p. 1097-105.
-
(2012)
Advances in Neural Information Processing Systems 25
, pp. 1097-1105
-
-
Krizhevsky, A.1
Sutskever, I.2
Hinton, G.E.3
-
29
-
-
78650814453
-
Pattern recognition in histopathological images: An ICPR 2010 contest
-
In: Ünay D, Çataltepe Z, Aksoy S, editors.: Springer Berlin Heidelberg
-
Gurcan MN, Madabhushi A, Rajpoot N. Pattern recognition in histopathological images: An ICPR 2010 contest. In: Ünay D, Çataltepe Z, Aksoy S, editors. Recognizing Patterns in Signals, Speech, Images and Videos. (Lecture Notes in Computer Science).Vol. 6388: Springer Berlin Heidelberg; 2010. p. 226-34.
-
(2010)
Recognizing Patterns in Signals, Speech, Images and Videos. (Lecture Notes in Computer Science)
, vol.6388
, pp. 226-234
-
-
Gurcan, M.N.1
Madabhushi, A.2
Rajpoot, N.3
-
30
-
-
84555179528
-
A boosted Bayesian multiresolution classifier for prostate cancer detection from digitized needle biopsies
-
Doyle S, Feldman M, Tomaszewski J, Madabhushi A. A boosted Bayesian multiresolution classifier for prostate cancer detection from digitized needle biopsies. IEEE Trans Biomed Eng 2012;59:1205-18.
-
(2012)
IEEE Trans Biomed Eng
, vol.59
, pp. 1205-1218
-
-
Doyle, S.1
Feldman, M.2
Tomaszewski, J.3
Madabhushi, A.4
-
33
-
-
71149119164
-
Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
-
New York, USA: ACM
-
Lee H, Grosse R, Ranganath R, Ng A. Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations. In: Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09. New York, USA: ACM; 2009. p. 609-16.
-
(2009)
Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09
, pp. 609-616
-
-
Lee, H.1
Grosse, R.2
Ranganath, R.3
Ng, A.4
-
34
-
-
77955998889
-
Convolutional networks and applications in vision
-
May 30-June 2, 2010, Paris, France
-
LeCun Y, Kavukcuoglu K, Farabet C. Convolutional Networks and Applications in Vision. In: International Symposium on Circuits and Systems (ISCAS 2010), May 30-June 2, 2010, Paris, France; 2010. p. 253-6.
-
(2010)
International Symposium on Circuits and Systems (ISCAS 2010)
, pp. 253-256
-
-
LeCun, Y.1
Kavukcuoglu, K.2
Farabet, C.3
-
36
-
-
84890527827
-
-
In: IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2013, Vancouver, BC, Canada; 26-31 May
-
Dahl GE, Sainath TN, Hinton GE. Improving Deep Neural Networks for LVCSR Using Rectified Linear Units and Dropout. In: IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2013, Vancouver, BC, Canada; 26-31 May, 2013. p. 8609-13.
-
(2013)
Improving Deep Neural Networks for LVCSR Using Rectified Linear Units and Dropout
, pp. 8609-8613
-
-
Dahl, G.E.1
Sainath, T.N.2
Hinton, G.E.3
-
37
-
-
84862294866
-
Deep sparse rectifier neural networks
-
In: Gordon GJ, Dunson DB, editors.. Workshop and Conference Proceedings;. [Journal of Machine Learning Research]
-
Glorot X, Bordes A, Bengio Y. Deep sparse rectifier neural networks. In: Gordon GJ, Dunson DB, editors. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics (AISTATS-11). Vol. 15. Workshop and Conference Proceedings; 2011. p. 315-23. [Journal of Machine Learning Research].
-
(2011)
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics (AISTATS-11)
, vol.15
, pp. 315-323
-
-
Glorot, X.1
Bordes, A.2
Bengio, Y.3
-
38
-
-
84904163933
-
Dropout: A simple way to prevent neural networks from overfitting
-
Srivastava N, Hinton GE, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: A simple way to prevent neural networks from overfitting. J Mach Learn Res 2014;15:1929-58.
-
(2014)
J Mach Learn Res
, vol.15
, pp. 1929-1958
-
-
Srivastava, N.1
Hinton, G.E.2
Krizhevsky, A.3
Sutskever, I.4
Salakhutdinov, R.5
-
39
-
-
80052250414
-
Adaptive subgradient methods for online learning and stochastic optimization
-
Duchi JC, Hazan E, Singer Y. Adaptive subgradient methods for online learning and stochastic optimization. J Mach Learn Res 2011;12:2121-59.
-
(2011)
J Mach Learn Res
, vol.12
, pp. 2121-2159
-
-
Duchi, J.C.1
Hazan, E.2
Singer, Y.3
-
40
-
-
0031948563
-
Comparison of the prognostic value of scarff-bloom-richardson and nottingham histological grades in a series of 825 cases of breast cancer: Major importance of the mitotic count as a component of both grading systems
-
Genestie C1, Zafrani B, Asselain B, Fourquet A, Rozan S, Validire P, et al. Comparison of the prognostic value of scarff-bloom-richardson and nottingham histological grades in a series of 825 cases of breast cancer: Major importance of the mitotic count as a component of both grading systems. Anticancer Res 1998;18:571-6.
-
(1998)
Anticancer Res
, vol.18
, pp. 571-576
-
-
Genestie, C.1
Zafrani, B.2
Asselain, B.3
Fourquet, A.4
Rozan, S.5
Validire, P.6
-
41
-
-
7444271447
-
Gleason grading and prognostic factors in carcinoma of the prostate
-
Humphrey PA. Gleason grading and prognostic factors in carcinoma of the prostate. Mod Pathol 2004;17:292-306.
-
(2004)
Mod Pathol
, vol.17
, pp. 292-306
-
-
Humphrey, P.A.1
-
42
-
-
84900449424
-
Methods for nuclei detection, segmentation, and classification in digital histopathology: A review-current status and future potential
-
Irshad H, Veillard A, Roux L, Racoceanu D. Methods for nuclei detection, segmentation, and classification in digital histopathology: A review-current status and future potential. IEEE Rev Biomed Eng 2014;7:97-114.
-
(2014)
IEEE Rev Biomed Eng
, vol.7
, pp. 97-114
-
-
Irshad, H.1
Veillard, A.2
Roux, L.3
Racoceanu, D.4
-
43
-
-
0034890852
-
Quantification of histochemical staining by color deconvolution
-
Ruifrok AC, Johnston DA. Quantification of histochemical staining by color deconvolution. Anal Quant Cytol Histol 2001;23:291-9.
-
(2001)
Anal Quant Cytol Histol
, vol.23
, pp. 291-299
-
-
Ruifrok, A.C.1
Johnston, D.A.2
-
44
-
-
81055146760
-
Systematic analysis of breast cancer morphology uncovers stromal features associated with survival
-
Beck AH, Sangoi AR, Leung S, Marinelli RJ, Nielsen TO, van de Vijver MJ, et al. Systematic analysis of breast cancer morphology uncovers stromal features associated with survival. Sci Transl Med 2011;3:108ra113.
-
(2011)
Sci Transl Med
, vol.3
, pp. 108ra113
-
-
Beck, A.H.1
Sangoi, A.R.2
Leung, S.3
Marinelli, R.J.4
Nielsen, T.O.5
Van De Vijver, M.J.6
-
45
-
-
79955752231
-
Incorporating domain knowledge for tubule detection in breast histopathology using o'callaghan neighborhoods
-
SPIE
-
Basavanhally A, Yu E, Xu J, Ganesan S, Feldman M, Tomaszewski J, et al. Incorporating domain knowledge for tubule detection in breast histopathology using o'callaghan neighborhoods. In: SPIE Medical Imaging. (Computer-Aided Diagnosis). Vol. 7963. SPIE; 2011. p. 796310.
-
(2011)
SPIE Medical Imaging. (Computer-Aided Diagnosis)
, vol.7963
, pp. 796310
-
-
Basavanhally, A.1
Yu, E.2
Xu, J.3
Ganesan, S.4
Feldman, M.5
Tomaszewski, J.6
-
47
-
-
77953803946
-
Expectation-maximization-driven geodesic active contour with overlap resolution (EMaGACOR): Application to lymphocyte segmentation on breast cancer histopathology
-
Fatakdawala H, Xu J, Basavanhally A, Bhanot G, Ganesan S, Feldman M, et al. Expectation-maximization-driven geodesic active contour with overlap resolution (EMaGACOR): Application to lymphocyte segmentation on breast cancer histopathology. IEEE Trans Biomed Eng 2010;57:1676-89.
-
(2010)
IEEE Trans Biomed Eng
, vol.57
, pp. 1676-1689
-
-
Fatakdawala, H.1
Xu, J.2
Basavanhally, A.3
Bhanot, G.4
Ganesan, S.5
Feldman, M.6
-
48
-
-
84872579310
-
Learning to detect cells using non-overlapping extremal regions
-
In: Ayache N, editor.. (Lecture Notes in Computer Science). MICCAI, Springer
-
Arteta C, Lempitsky V, Noble JA, Zisserman A. Learning to detect cells using non-overlapping extremal regions. In: Ayache N, editor. International Conference on Medical Image Computing and Computer Assisted Intervention. (Lecture Notes in Computer Science). MICCAI, Springer; 2012. p. 348-56.
-
(2012)
International Conference on Medical Image Computing and Computer Assisted Intervention
, pp. 348-356
-
-
Arteta, C.1
Lempitsky, V.2
Noble, J.A.3
Zisserman, A.4
-
50
-
-
84923019397
-
Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features
-
Wang H, Cruz-Roa A, Basavanhally A, Gilmore H, Shih N, Feldman M, et al. Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features. J Med Imaging (Bellingham) 2014;1:034003.
-
(2014)
J Med Imaging (Bellingham)
, vol.1
, pp. 034003
-
-
Wang, H.1
Cruz-Roa, A.2
Basavanhally, A.3
Gilmore, H.4
Shih, N.5
Feldman, M.6
-
51
-
-
45049083228
-
WND-CHARM: Multi-purpose image classification using compound image transforms
-
Orlov N, Shamir L, Macura T, Johnston J, Eckley DM, Goldberg IG. WND-CHARM: Multi-purpose image classification using compound image transforms. Pattern Recognit Lett 2008;29:1684-93.
-
(2008)
Pattern Recognit Lett
, vol.29
, pp. 1684-1693
-
-
Orlov, N.1
Shamir, L.2
Macura, T.3
Johnston, J.4
Eckley, D.M.5
Goldberg, I.G.6
-
52
-
-
84906489074
-
Visualizing and understanding convolutional networks
-
Zurich, Switzerland, September 6-12, 2014, Proceedings, Part I
-
Zeiler MD, Fergus R. Visualizing and Understanding Convolutional Networks. In: Computer Vision-ECCV 2014-13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part I; 2014. p. 818-33.
-
(2014)
Computer Vision-ECCV 2014-13th European Conference
, pp. 818-833
-
-
Zeiler, M.D.1
Fergus, R.2
-
53
-
-
85009286008
-
-
Tech. Rep. 1341, University of Montreal, June 2009. ICML 2009 Workshop on Learning Feature Hierarchies, Montréal, Canada
-
Erhan D, Bengio Y, Courville A, Vincent P. Visualizing Higher-layer Features of a Deep Network. Tech. Rep. 1341, University of Montreal, June 2009. ICML 2009 Workshop on Learning Feature Hierarchies, Montréal, Canada; 2009.
-
(2009)
Visualizing Higher-layer Features of A Deep Network
-
-
Erhan, D.1
Bengio, Y.2
Courville, A.3
Vincent, P.4
|