-
1
-
-
80155172209
-
-
American Cancer Society,accessed: 2015-10-23
-
American Cancer Society, "Melanoma skin cancer, " http: //cancer. org/melanoma-skin-cancer-pdf, accessed: 2015-10-23.
-
Melanoma Skin Cancer
-
-
-
2
-
-
84869084264
-
Computerized analysis of pigmented skin lesions: A review
-
K. Korotkov and R. Garcia, "Computerized analysis of pigmented skin lesions: A review, " Artificial Intelligence in Medicine, vol. 56, no. 2, pp. 69-90, 2012.
-
(2012)
Artificial Intelligence in Medicine
, vol.56
, Issue.2
, pp. 69-90
-
-
Korotkov, K.1
Garcia, R.2
-
3
-
-
84860322847
-
A systematic review of worldwide incidence of nonmelanoma skin cancer
-
A. Lomas, J. Leonardi-Bee, and F. Bath-Hextall, "A systematic review of worldwide incidence of nonmelanoma skin cancer, " Br. J. Dermatol., vol. 166, no. 5, pp. 1069-1080, 2012.
-
(2012)
Br. J. Dermatol.
, vol.166
, Issue.5
, pp. 1069-1080
-
-
Lomas, A.1
Leonardi-Bee, J.2
Bath-Hextall, F.3
-
4
-
-
85043454425
-
A color and texture based hierarchical K-NN approach to the classification of non-melanoma skin lesions
-
L. Ballerini, R. B. Fisher, B. Aldridge, and J. Rees, "A color and texture based hierarchical K-NN approach to the classification of non-melanoma skin lesions, " Color Medical Image Analysis, vol. 6, pp. 63-86, 2013.
-
(2013)
Color Medical Image Analysis
, vol.6
, pp. 63-86
-
-
Ballerini, L.1
Fisher, R.B.2
Aldridge, B.3
Rees, J.4
-
5
-
-
84992513757
-
Hierarchical classification of ten skin lesion classes
-
C. D. Leo, V. Bevilacqua, L. Ballerini, R. Fisher, B. Aldridge, and J. Rees, "Hierarchical classification of ten skin lesion classes, " Proc. Dundee Medical Image Analysis Workshop, 2015.
-
(2015)
Proc. Dundee Medical Image Analysis Workshop
-
-
Leo, C.D.1
Bevilacqua, V.2
Ballerini, L.3
Fisher, R.4
Aldridge, B.5
Rees, J.6
-
6
-
-
84919941351
-
Four-class classification of skin lesions with task decomposition strategy
-
K. Shimizu et al., "Four-class classification of skin lesions with task decomposition strategy, " IEEE TBE, vol. 62, no. 1, pp. 274-283, 2015.
-
(2015)
IEEE TBE
, vol.62
, Issue.1
, pp. 274-283
-
-
Shimizu, K.1
-
7
-
-
84883830807
-
Depth data improves skin lesion segmentation
-
X. Li et al., "Depth data improves skin lesion segmentation, " in MICCAI, vol. 5762, 2009, pp. 1100-1107.
-
(2009)
MICCAI
, vol.5762
, pp. 1100-1107
-
-
Li, X.1
-
8
-
-
84947041871
-
ImageNet large scale visual recognition challenge
-
O. Russakovsky et al., "ImageNet large scale visual recognition challenge, " IJCV, vol. 115, no. 3, pp. 211-252, 2015.
-
(2015)
IJCV
, vol.115
, Issue.3
, pp. 211-252
-
-
Russakovsky, O.1
-
9
-
-
84906332834
-
DeCAF: A deep convolutional activation feature for generic visual recognition
-
J. Donahue et al., "DeCAF: A deep convolutional activation feature for generic visual recognition, " ICML, vol. 32, pp. 647-655, 2014.
-
(2014)
ICML
, vol.32
, pp. 647-655
-
-
Donahue, J.1
-
10
-
-
84952004763
-
Deep learning, sparse coding, and SVM for melanoma recognition in dermoscopy images
-
N. Codella, J. Cai, M. Abedini, R. Garnavi, A. Halpern, and J. R. Smith, "Deep learning, sparse coding, and SVM for melanoma recognition in dermoscopy images, " in MICCAI MLMI, vol. 9352, 2015, pp. 118-126.
-
(2015)
MICCAI MLMI
, vol.9352
, pp. 118-126
-
-
Codella, N.1
Cai, J.2
Abedini, M.3
Garnavi, R.4
Halpern, A.5
Smith, J.R.6
-
11
-
-
85083953063
-
Very deep convolutional networks for large-scale image recognition
-
K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition, " ICLR, 2015.
-
(2015)
ICLR
-
-
Simonyan, K.1
Zisserman, A.2
-
12
-
-
84876231242
-
ImageNet classification with deep convolutional neural networks
-
A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet classification with deep convolutional neural networks, " in NIPS, 2012, pp. 1097-1105.
-
(2012)
NIPS
, pp. 1097-1105
-
-
Krizhevsky, A.1
Sutskever, I.2
Hinton, G.E.3
-
13
-
-
85083951635
-
OverFeat: Integrated recognition, localization and detection using convolutional networks
-
P. Sermanet et al., "OverFeat: Integrated recognition, localization and detection using convolutional networks, " ICLR, 2014.
-
(2014)
ICLR
-
-
Sermanet, P.1
-
14
-
-
84913580146
-
Caffe: Convolutional architecture for fast feature embedding
-
Y. Jia et al., "Caffe: Convolutional architecture for fast feature embedding, " ACM Conference on Multimedia, pp. 675-678, 2014.
-
(2014)
ACM Conference on Multimedia
, pp. 675-678
-
-
Jia, Y.1
|