-
2
-
-
85014841160
-
Pap smear image classification using convolutional neural network
-
New York, NY, USA, ACM
-
K. Bora, M. Chowdhury, L. B. Mahanta, M. K. Kundu, and A. K. Das. Pap smear image classification using convolutional neural network. In Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing, ICVGIP '16, pages 55:1-55:8, New York, NY, USA, 2016. ACM.
-
(2016)
Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing, ICVGIP '16
, pp. 1-8
-
-
Bora, K.1
Chowdhury, M.2
Mahanta, L.B.3
Kundu, M.K.4
Das, A.K.5
-
5
-
-
84958589374
-
Deep residual learning for image recognition
-
abs/1512.03385
-
K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. CoRR, abs/1512.03385, 2015.
-
(2015)
CoRR
-
-
He, K.1
Zhang, X.2
Ren, S.3
Sun, J.4
-
6
-
-
85017611781
-
Diagnosing cervical cell images using pre-trained convolutional neural network as feature extractor
-
Feb.
-
J. Hyeon, H. J. Choi, B. D. Lee, and K. N. Lee. Diagnosing cervical cell images using pre-trained convolutional neural network as feature extractor. In 2017 IEEE International Conference on Big Data and Smart Computing (BigComp), pages 390-393, Feb 2017.
-
(2017)
2017 IEEE International Conference on Big Data and Smart Computing (BigComp)
, pp. 390-393
-
-
Hyeon, J.1
Choi, H.J.2
Lee, B.D.3
Lee, K.N.4
-
7
-
-
84946590546
-
Batch normalization: Accelerating deep network training by reducing internal covariate shift
-
abs/1502.03167
-
S. Ioffe and C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. CoRR, abs/1502.03167, 2015.
-
(2015)
CoRR
-
-
Ioffe, S.1
Szegedy, C.2
-
8
-
-
85083951076
-
Adam: A method for stochastic optimization
-
abs/1412.6980
-
D. P. Kingma and J. Ba. Adam: A method for stochastic optimization. CoRR, abs/1412.6980, 2014.
-
(2014)
CoRR
-
-
Kingma, D.P.1
Ba, J.2
-
10
-
-
84919941026
-
A structural texture approach for characterising malignancy associated changes in pap smears based on mean-shift and the watershed transform
-
Aug.
-
A. Mehnert, R. Moshavegh, K. Sujathan, P. Malm, and E. Bengtsson. A structural texture approach for characterising malignancy associated changes in pap smears based on mean-shift and the watershed transform. In 2014 22nd International Conference on Pattern Recognition, pages 1189-1193, Aug 2014.
-
(2014)
2014 22nd International Conference on Pattern Recognition
, pp. 1189-1193
-
-
Mehnert, A.1
Moshavegh, R.2
Sujathan, K.3
Malm, P.4
Bengtsson, E.5
-
13
-
-
84947041871
-
Imagenet large scale visual recognition challenge
-
12
-
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. Berg, and L. Fei-Fei. Imagenet large scale visual recognition challenge. International Journal of Computer Vision, 115(3):211-252, 12 2015.
-
(2015)
International Journal of Computer Vision
, vol.115
, Issue.3
, pp. 211-252
-
-
Russakovsky, O.1
Deng, J.2
Su, H.3
Krause, J.4
Satheesh, S.5
Ma, S.6
Huang, Z.7
Karpathy, A.8
Khosla, A.9
Bernstein, M.10
Berg, A.11
Fei-Fei, L.12
-
15
-
-
84933585162
-
Very deep convolutional networks for large-scale image recognition
-
abs/1409.1556
-
K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. CoRR, abs/1409.1556, 2014.
-
(2014)
CoRR
-
-
Simonyan, K.1
Zisserman, A.2
-
16
-
-
65649138430
-
A systematic analysis of performance measures for classification tasks
-
M. Sokolova and G. Lapalme. A systematic analysis of performance measures for classification tasks. Information Processing and Management, 45(4):427-437, 2009.
-
(2009)
Information Processing and Management
, vol.45
, Issue.4
, pp. 427-437
-
-
Sokolova, M.1
Lapalme, G.2
-
17
-
-
84904163933
-
Dropout: A simple way to prevent neural networks from overfitting
-
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov. Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15:1929-1958, 2014.
-
(2014)
Journal of Machine Learning Research
, vol.15
, pp. 1929-1958
-
-
Srivastava, N.1
Hinton, G.2
Krizhevsky, A.3
Sutskever, I.4
Salakhutdinov, R.5
-
18
-
-
85028984678
-
-
WHO. Accessed: 2017-07-03
-
WHO. Cervical cancer. http://www.who.int/cancer/prevention/diagnosis-screening/cervical-cancer/en/, 2017. Accessed: 2017-07-03.
-
(2017)
Cervical Cancer
-
-
-
20
-
-
84896459209
-
-
International Agency for Research on Cancer/World Health Organization
-
S. W. Wild P.C. World Cancer Report 2014. International Agency for Research on Cancer/World Health Organization, 2014.
-
(2014)
World Cancer Report 2014
-
-
Wild, S.W.P.C.1
-
21
-
-
85035807487
-
Deeppap: Deep convolutional networks for cervical cell classification
-
L. Zhang, L. Lu, I. Nogues, R. Summers, S. Liu, and J. Yao. Deeppap: Deep convolutional networks for cervical cell classification. IEEE Journal of Biomedical and Health Informatics, PP(99):1-1, 2017.
-
(2017)
IEEE Journal of Biomedical and Health Informatics
, vol.PP
, Issue.99
, pp. 1
-
-
Zhang, L.1
Lu, L.2
Nogues, I.3
Summers, R.4
Liu, S.5
Yao, J.6
|