[1] Ferlay J, Soerjomataram I, Dikshit R, et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012[J]. International Journal of Cancer, 2015, 136(5): 359-386.
Ferlay J, Soerjomataram I, Dikshit R, et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012[J]. International Journal of Cancer, 2015, 136(5): 359-386.
[2] Vodinh T, Stokes D L, Wabuyele M B, et al. A hyperspectral imaging system for in vivo optical diagnostics[J]. IEEE Engineering in Medicine and Biology Magazine, 2004, 23(5): 40-49.
Vodinh T, Stokes D L, Wabuyele M B, et al. A hyperspectral imaging system for in vivo optical diagnostics[J]. IEEE Engineering in Medicine and Biology Magazine, 2004, 23(5): 40-49.
[3] Luo B, Zhang L. Robust autodial morphological profiles for the classification of high-resolution satellite images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 52(2): 1451-1462.
Luo B, Zhang L. Robust autodial morphological profiles for the classification of high-resolution satellite images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 52(2): 1451-1462.
[4] 侯榜焕, 姚敏立, 王榕, 等. 面向高光谱图像分类的空谱半监督局部判别分析[J]. 光学学报, 2017, 37(7): 0728002.
侯榜焕, 姚敏立, 王榕, 等. 面向高光谱图像分类的空谱半监督局部判别分析[J]. 光学学报, 2017, 37(7): 0728002.
Hou B H, Yao M L, Wang R, et al. Spatial-spectral semi-supervised local discriminant analysis for heperspectral image classification[J]. Acta Optica Sinica, 2017, 37(7): 0728002.
Hou B H, Yao M L, Wang R, et al. Spatial-spectral semi-supervised local discriminant analysis for heperspectral image classification[J]. Acta Optica Sinica, 2017, 37(7): 0728002.
[5] Suzuki Y, Okamoto H, Takahashi M, et al. Mapping the spatial distribution of botanical composition and herbage mass in pastures using hyperspectral imaging[J]. Grassland Science, 2012, 58(1): 1-7.
Suzuki Y, Okamoto H, Takahashi M, et al. Mapping the spatial distribution of botanical composition and herbage mass in pastures using hyperspectral imaging[J]. Grassland Science, 2012, 58(1): 1-7.
[6] 候华毅, 方朝晖, 张元志, 等. 皮肤胆固醇无创光谱检测模拟和在体实验研究[J]. 中国激光, 2016, 43(9): 0907001.
候华毅, 方朝晖, 张元志, 等. 皮肤胆固醇无创光谱检测模拟和在体实验研究[J]. 中国激光, 2016, 43(9): 0907001.
Hou H Y, Fang Z H, Zhang Y Z, et al. Simulation an in vivo experimental study on noninvasive spectral detection of skin cholesterol[J]. Chinese Journal of Lasers, 2016, 43(9): 0907001.
Hou H Y, Fang Z H, Zhang Y Z, et al. Simulation an in vivo experimental study on noninvasive spectral detection of skin cholesterol[J]. Chinese Journal of Lasers, 2016, 43(9): 0907001.
[7] 董安国, 李佳逊, 张蓓, 等. 基于谱聚类和稀疏表示的高光谱图像分类算法[J]. 光学学报, 2017, 37(8): 0828005.
董安国, 李佳逊, 张蓓, 等. 基于谱聚类和稀疏表示的高光谱图像分类算法[J]. 光学学报, 2017, 37(8): 0828005.
Dong A G, Li J X, Zhang B, et al. Hyperspectral image classification algorithm based on spectral clustering and sparse representation[J]. Acta Optica Sinica, 2017, 37(8): 0828005.
Dong A G, Li J X, Zhang B, et al. Hyperspectral image classification algorithm based on spectral clustering and sparse representation[J]. Acta Optica Sinica, 2017, 37(8): 0828005.
[8] AkbariH,
Halig LV,
Zhang HZ, et al.
Detection of cancer metastasis using a novel macroscopic hyperspectral method[C]. SPIE,
2012,
8317:
831711.
AkbariH,
Halig LV,
Zhang HZ, et al.
Detection of cancer metastasis using a novel macroscopic hyperspectral method[C]. SPIE,
2012,
8317:
831711.
[9] 刘洪英, 顾文荃, 李庆利, 等. 超光谱成像技术应用于神经分类的研究[J]. 光谱学与光谱分析, 2015, 35(1): 38-43.
刘洪英, 顾文荃, 李庆利, 等. 超光谱成像技术应用于神经分类的研究[J]. 光谱学与光谱分析, 2015, 35(1): 38-43.
Liu H Y, Gu W Q, Li Q L, et al. Nerve classification with hyperspectral imaging technology[J]. Spectroscopy and Spectral Analysis, 2015, 35(1): 38-43.
Liu H Y, Gu W Q, Li Q L, et al. Nerve classification with hyperspectral imaging technology[J]. Spectroscopy and Spectral Analysis, 2015, 35(1): 38-43.
[10] Zhu S, Su K, Liu Y, et al. Identification of cancerous gastric cells based on common features extracted from hyperspectral microscopic images[J]. Biomedical Optics Express, 2015, 6(4): 1135-1145.
Zhu S, Su K, Liu Y, et al. Identification of cancerous gastric cells based on common features extracted from hyperspectral microscopic images[J]. Biomedical Optics Express, 2015, 6(4): 1135-1145.
[11] Akbari H, Halig L V, Schuster D M, et al. Hyperspectral imaging and quantitative analysis for prostate cancer detection[J]. Journal of Biomedical Optics, 2012, 17(7): 076005.
Akbari H, Halig L V, Schuster D M, et al. Hyperspectral imaging and quantitative analysis for prostate cancer detection[J]. Journal of Biomedical Optics, 2012, 17(7): 076005.
[12] Gerstner A O, Laffers W, Bootz F, et al. Hyperspectral imaging of mucosal surfaces in patients[J]. Journal of Biophotonics, 2012, 5(3): 255-262.
Gerstner A O, Laffers W, Bootz F, et al. Hyperspectral imaging of mucosal surfaces in patients[J]. Journal of Biophotonics, 2012, 5(3): 255-262.
[13] Liu Z, Wang H, Li Q. Tongue tumor detection in medical hyperspectral images[J]. Sensors, 2012, 12(1): 162-174.
Liu Z, Wang H, Li Q. Tongue tumor detection in medical hyperspectral images[J]. Sensors, 2012, 12(1): 162-174.
[14] 原江伟, 张春光, 王号, 等. 基于声光可调滤波器的肺癌组织快速显微光谱成像[J]. 中国激光, 2018, 45(4): 0407003.
原江伟, 张春光, 王号, 等. 基于声光可调滤波器的肺癌组织快速显微光谱成像[J]. 中国激光, 2018, 45(4): 0407003.
Yuan J W, Zhang C G, Wang H, et al. Rapid microscopic spectral imaging of lung cancer tissue based on acousto-optic tunable filter[J]. Chinese Journal of Lasers, 2018, 45(4): 0407003.
Yuan J W, Zhang C G, Wang H, et al. Rapid microscopic spectral imaging of lung cancer tissue based on acousto-optic tunable filter[J]. Chinese Journal of Lasers, 2018, 45(4): 0407003.
[15] Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786): 504-507.
Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786): 504-507.
[16] LecunY,
BottouL,
BengioY, et al.
Gradient-based learning applied to document recognition[C]. Proceedings of the IEEE,
1998,
86(
11):
2278-
2324.
LecunY,
BottouL,
BengioY, et al.
Gradient-based learning applied to document recognition[C]. Proceedings of the IEEE,
1998,
86(
11):
2278-
2324.
[17] KarenS,
AndrewZ.
Very deep convolutional networks for large-scale image recognition[C]. Proceedings of ICLR,
2015:
1-
14.
KarenS,
AndrewZ.
Very deep convolutional networks for large-scale image recognition[C]. Proceedings of ICLR,
2015:
1-
14.
[18] Hinton G E, Srivastava N, Krizhevsky A, et al. Improving neural networks by preventing co-adaptation of feature detectors[J]. Computer Science, 2012, 3(4): 212-223.
Hinton G E, Srivastava N, Krizhevsky A, et al. Improving neural networks by preventing co-adaptation of feature detectors[J]. Computer Science, 2012, 3(4): 212-223.
[19] Zuo Z, Shuai B, Wang G, et al. Learning contextual dependence with convolutional hierarchical recurrent neural networks[J]. IEEE Transactions on Image Processing, 2016, 25(7): 2983-2996.
Zuo Z, Shuai B, Wang G, et al. Learning contextual dependence with convolutional hierarchical recurrent neural networks[J]. IEEE Transactions on Image Processing, 2016, 25(7): 2983-2996.