Abstract

An outstanding challenge for immunology is the classification of immune cells in a label-free fashion with high speed. For this purpose, optical techniques such as Raman spectroscopy or digital holographic microscopy have been used successfully to identify immune cell subsets. To achieve high accuracy, these techniques require a post-processing step using linear methods of multivariate processing, such as principal component analysis. Here we demonstrate for the first time a comparison between artificial neural networks and principal component analysis (PCA) to classify the key granulocyte cell lineages of neutrophils and eosinophils using both digital holographic microscopy and Raman spectroscopy. Artificial neural networks can offer advantages in terms of classification accuracy and speed over a PCA approach. We conclude that digital holographic microscopy with convolutional neural networks based analysis provides a route to a robust, stand-alone and high-throughput hemogram with a classification accuracy of 91.3 % at a throughput rate of greater than 100 cells per second.

Published by The Optical Society under the terms of the Creative Commons Attribution 4.0 License. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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References

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2018 (1)

M. J. Huttunen, A. Hassan, C. W. McCloskey, S. Fasih, J. Upham, B. C. Vanderhyden, R. W. Boyd, and S. Murugkar, “Automated classification of multiphoton microscopy images of ovarian tissue using deep learning,” J. Biomed. Opt. 23, 066002 (2018).
[Crossref]

2017 (5)

V. O. Baron, M. Chen, S. O. Clark, A. Williams, R. J. H. Hammond, K. Dholakia, and S. H. Gillespie, “Label-free optical vibrational spectroscopy to detect the metabolic state of M. tuberculosis cells at the site of disease,” Sci. Rep. 7, 9844 (2017).
[Crossref] [PubMed]

X. Wang, W. Yang, J. Weinreb, J. Han, Q. Li, X. Kong, Y. Yan, Z. Ke, B. Luo, T. Liu, and L. Wang, “Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning,” Sci. Reports 7, 15415 (2017).
[Crossref]

A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature 542, 115–118 (2017).
[Crossref] [PubMed]

Y. J. Jo, S. Park, J. H. Jung, J. Yoon, H. Joo, M. H. Kim, S. J. Kang, M. C. Choi, S. Y. Lee, and Y. K. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3, e1700606 (2017).
[Crossref] [PubMed]

N. McReynolds, F. G. M. Cooke, M. Chen, S. J. Powis, and K. Dholakia, “Multimodal discrimination of immune cells using a combination of Raman spectroscopy and digital holographic microscopy,” Sci. Reports 7, 43631 (2017).
[Crossref]

2016 (4)

S. M. Lyons, E. Alizadeh, J. Mannheimer, K. Schuamberg, J. Castle, B. Schroder, P. Turk, D. Thamm, and A. Prasad, “Changes in cell shape are correlated with metastatic potential in murine and human osteosarcomas,” Biol. Open 5, 289–299 (2016).
[Crossref] [PubMed]

J. Arevalo, F. A. González, R. Ramos-Pollán, J. L. Oliveira, and M. A. Guevara Lopez, “Representation learning for mammography mass lesion classification with convolutional neural networks,” Comput. Methods Programs Biomed. 127, 248–257 (2016).
[Crossref] [PubMed]

B. Microbiana, D. Hidalgo, M. Anthimopoilos, S. Christodoulidis, L. Ebner, A. Christe, and S. Mougiakakou, “Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network,” IEEE Transactions on Med. Imaging 35, 1207–1216 (2016).
[Crossref]

L. Zhang and P. Suganthan, “A survey of randomized algorithms for training neural networks,” Inf. Sci. 364-365, 146–155 (2016).
[Crossref]

2015 (2)

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

M. Chen, N. McReynolds, E. C. Campbell, M. Mazilu, J. Barbosa, K. Dholakia, and S. J. Powis, “The Use of Wavelength Modulated Raman Spectroscopy in Label-Free Identification of T Lymphocyte Subsets, Natural Killer Cells and Dendritic Cells,” Plos One 5, e0125158 (2015).
[Crossref]

2014 (1)

C. Fontanella, S. Bolzonello, B. Lederer, and G. Aprile, “Management of breast cancer patients with chemotherapy-induced neutropenia or febrile neutropenia,” Breast Care 9, 239–245 (2014).
[Crossref] [PubMed]

2013 (1)

C. J. Thomas and K. Schroder, “Pattern recognition receptor function in neutrophils,” Trends Immunol. 34, 317–328 (2013).
[Crossref] [PubMed]

2012 (2)

A. Ramoji, U. Neugebauer, T. Bocklitz, M. Foerster, M. Kiehntopf, M. Bauer, and J. Popp, “Toward a spectroscopic hemogram: Raman spectroscopic differentiation of the two most abundant leukocytes from peripheral blood,” Anal. Chem. 84, 5335–5342 (2012).
[Crossref] [PubMed]

M. A. Kovach and T. J. Standiford, “The function of neutrophils in sepsis,” Curr. Opin. Infect. Dis. 25, 321–327 (2012).
[Crossref] [PubMed]

2011 (1)

U. Orhan, M. Hekim, and M. Ozer, “Eeg signals classification using the k-means clustering and a multilayer perceptron neural network model,” Expert Syst. with Appl. 38, 13475–13481 (2011).
[Crossref]

2010 (3)

A. C. De Luca, M. Mazilu, A. Riches, C. S. Herrington, and K. Dholakia, “Online fluorescence suppression in modulated raman spectroscopy,” Anal. Chem. 82, 738–745 (2010).
[Crossref]

G. C. Cawley and N. L. Talbot, “On over-fitting in model selection and subsequent selection bias in performance evaluation,” J. Mach. Learn. Res. 11, 2079–2107 (2010).

N. Ganesan, “Application of neural networks in diagnosing cancer disease using demographic data,” Int. J. Comput. Appl. ( 09751, 76–85 (2010).

2009 (1)

P. Ruutu, T. Ruutu, P. Vuopio, T. U. Kosunen, and A. de la Chapelle, “Function of Neutrophils in Preleukaemia,” Scand. J. Haematol. 18, 317–325 (2009).
[Crossref]

2008 (1)

S. Prasad and L. M. Bruce, “Limitations of Principal Components Analysis for Hyperspectral Target Recognition,” Geosci. Remote Sens. Lett., IEEE 5, 625–629 (2008).
[Crossref]

2006 (1)

H. Yan, Y. Jiang, J. Zheng, C. Peng, and Q. Li, “A multilayer perceptron-based medical decision support system for heart disease diagnosis,” Expert Syst. with Appl. 30, 272–281 (2006).
[Crossref]

2005 (1)

R. M. Pascual and S. P. Peters, “Airway remodeling contributes to the progressive loss of lung function in asthma: an overview,” The J. allergy clinical immunology 116, 477–486 (2005).
[Crossref]

2004 (1)

A. D. Klion and T. B. Nutman, “The role of eosinophils in host defense against helminth parasites,” J. Allergy Clin. Immunol. 113, 30–37 (2004).
[Crossref] [PubMed]

2001 (3)

H. F. Rosenberg and J. B. Domachowske, “Eosinophils, eosinophil ribonucleases, and their role in host defense against respiratory virus pathogens,” J. Leukoc. Biol. 70, 691–698 (2001).
[PubMed]

D. Dombrowicz and M. Capron, “Eosinophils, allergy and parasites,” Curr. Opin. Immunol. 13, 716–720 (2001).
[Crossref] [PubMed]

J. R. MacKenzie, J. Mattes, L. A. Dent, and P. S. Foster, “Eosinophils promote allergic disease of the lung by regulating cd4+ th2 lymphocyte function,” The J. Immunol. 167, 3146–3155 (2001).
[Crossref]

2000 (1)

F. Tacchini-Cottier, C. Zweifel, Y. Belkaid, C. Mukankundiye, M. Vasei, P. Launois, G. Milon, and J. A. Louis, “An immunomodulatory function for neutrophils during the induction of a CD4+ Th2 response in BALB/c mice infected with Leishmania major,” The J. Immunol. 165, 2628–2636 (2000).
[Crossref] [PubMed]

1992 (1)

M. Capron and M. Capron, “Dual function of eosinophils in pathogenesis and protective immunity against parasites,” Memórias do Instituto Oswaldo Cruz 87, 83–89 (1992).
[Crossref]

1991 (1)

E. Griffin, L. Håkansson, H. Formgren, K. Jörgensen, C. Peterson, and P. Venge, “Blood eosinophil number and activity in relation to lung function in patients with asthma and with eosinophilia,” The J. allergy clinical immunology 87, 548–557 (1991).
[Crossref]

1980 (1)

J. E. Repine, C. C. Clawson, and F. C. Goetz, “Bactericidal function of neutrophils from patients with acute bacterial infections and from diabetics,” The J. infectious diseases 142, 869–875 (1980).
[Crossref]

Alizadeh, E.

S. M. Lyons, E. Alizadeh, J. Mannheimer, K. Schuamberg, J. Castle, B. Schroder, P. Turk, D. Thamm, and A. Prasad, “Changes in cell shape are correlated with metastatic potential in murine and human osteosarcomas,” Biol. Open 5, 289–299 (2016).
[Crossref] [PubMed]

Anthimopoilos, M.

B. Microbiana, D. Hidalgo, M. Anthimopoilos, S. Christodoulidis, L. Ebner, A. Christe, and S. Mougiakakou, “Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network,” IEEE Transactions on Med. Imaging 35, 1207–1216 (2016).
[Crossref]

Aprile, G.

C. Fontanella, S. Bolzonello, B. Lederer, and G. Aprile, “Management of breast cancer patients with chemotherapy-induced neutropenia or febrile neutropenia,” Breast Care 9, 239–245 (2014).
[Crossref] [PubMed]

Arevalo, J.

J. Arevalo, F. A. González, R. Ramos-Pollán, J. L. Oliveira, and M. A. Guevara Lopez, “Representation learning for mammography mass lesion classification with convolutional neural networks,” Comput. Methods Programs Biomed. 127, 248–257 (2016).
[Crossref] [PubMed]

Bai, G.

J. Chen, G. Bai, S. Liang, and Z. Li, “Automatic image cropping: A computational complexity study,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2016), pp. 507–515.

Barbosa, J.

M. Chen, N. McReynolds, E. C. Campbell, M. Mazilu, J. Barbosa, K. Dholakia, and S. J. Powis, “The Use of Wavelength Modulated Raman Spectroscopy in Label-Free Identification of T Lymphocyte Subsets, Natural Killer Cells and Dendritic Cells,” Plos One 5, e0125158 (2015).
[Crossref]

Baron, V. O.

V. O. Baron, M. Chen, S. O. Clark, A. Williams, R. J. H. Hammond, K. Dholakia, and S. H. Gillespie, “Label-free optical vibrational spectroscopy to detect the metabolic state of M. tuberculosis cells at the site of disease,” Sci. Rep. 7, 9844 (2017).
[Crossref] [PubMed]

V. O. Baron, M. Chen, S. O. Clark, A. Williams, K. Dholakia, and S. H. Gillespie, Detecting Phenotypically Resistant Mycobacterium tuberculosis Using Wavelength Modulated Raman Spectroscopy(SpringerNew York, 2018), pp. 41–50.

Bauer, M.

A. Ramoji, U. Neugebauer, T. Bocklitz, M. Foerster, M. Kiehntopf, M. Bauer, and J. Popp, “Toward a spectroscopic hemogram: Raman spectroscopic differentiation of the two most abundant leukocytes from peripheral blood,” Anal. Chem. 84, 5335–5342 (2012).
[Crossref] [PubMed]

Belkaid, Y.

F. Tacchini-Cottier, C. Zweifel, Y. Belkaid, C. Mukankundiye, M. Vasei, P. Launois, G. Milon, and J. A. Louis, “An immunomodulatory function for neutrophils during the induction of a CD4+ Th2 response in BALB/c mice infected with Leishmania major,” The J. Immunol. 165, 2628–2636 (2000).
[Crossref] [PubMed]

Bengio, Y.

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

Blau, H. M.

A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature 542, 115–118 (2017).
[Crossref] [PubMed]

Bocklitz, T.

A. Ramoji, U. Neugebauer, T. Bocklitz, M. Foerster, M. Kiehntopf, M. Bauer, and J. Popp, “Toward a spectroscopic hemogram: Raman spectroscopic differentiation of the two most abundant leukocytes from peripheral blood,” Anal. Chem. 84, 5335–5342 (2012).
[Crossref] [PubMed]

Bolzonello, S.

C. Fontanella, S. Bolzonello, B. Lederer, and G. Aprile, “Management of breast cancer patients with chemotherapy-induced neutropenia or febrile neutropenia,” Breast Care 9, 239–245 (2014).
[Crossref] [PubMed]

Boyd, R. W.

M. J. Huttunen, A. Hassan, C. W. McCloskey, S. Fasih, J. Upham, B. C. Vanderhyden, R. W. Boyd, and S. Murugkar, “Automated classification of multiphoton microscopy images of ovarian tissue using deep learning,” J. Biomed. Opt. 23, 066002 (2018).
[Crossref]

Bruce, L. M.

S. Prasad and L. M. Bruce, “Limitations of Principal Components Analysis for Hyperspectral Target Recognition,” Geosci. Remote Sens. Lett., IEEE 5, 625–629 (2008).
[Crossref]

Campbell, E. C.

M. Chen, N. McReynolds, E. C. Campbell, M. Mazilu, J. Barbosa, K. Dholakia, and S. J. Powis, “The Use of Wavelength Modulated Raman Spectroscopy in Label-Free Identification of T Lymphocyte Subsets, Natural Killer Cells and Dendritic Cells,” Plos One 5, e0125158 (2015).
[Crossref]

Capron, M.

D. Dombrowicz and M. Capron, “Eosinophils, allergy and parasites,” Curr. Opin. Immunol. 13, 716–720 (2001).
[Crossref] [PubMed]

M. Capron and M. Capron, “Dual function of eosinophils in pathogenesis and protective immunity against parasites,” Memórias do Instituto Oswaldo Cruz 87, 83–89 (1992).
[Crossref]

M. Capron and M. Capron, “Dual function of eosinophils in pathogenesis and protective immunity against parasites,” Memórias do Instituto Oswaldo Cruz 87, 83–89 (1992).
[Crossref]

Castle, J.

S. M. Lyons, E. Alizadeh, J. Mannheimer, K. Schuamberg, J. Castle, B. Schroder, P. Turk, D. Thamm, and A. Prasad, “Changes in cell shape are correlated with metastatic potential in murine and human osteosarcomas,” Biol. Open 5, 289–299 (2016).
[Crossref] [PubMed]

Cawley, G. C.

G. C. Cawley and N. L. Talbot, “On over-fitting in model selection and subsequent selection bias in performance evaluation,” J. Mach. Learn. Res. 11, 2079–2107 (2010).

Chandra, A.

A. Pal, A. Chaturvedi, U. Garain, A. Chandra, and R. Chatterjee, “Severity grading of psoriatic plaques using deep CNN based multi-task learning,” in 2016 23rd International Conference on Pattern Recognition (ICPR), (IEEE, 2016), pp. 1478–1483.

Chatterjee, R.

A. Pal, A. Chaturvedi, U. Garain, A. Chandra, and R. Chatterjee, “Severity grading of psoriatic plaques using deep CNN based multi-task learning,” in 2016 23rd International Conference on Pattern Recognition (ICPR), (IEEE, 2016), pp. 1478–1483.

Chaturvedi, A.

A. Pal, A. Chaturvedi, U. Garain, A. Chandra, and R. Chatterjee, “Severity grading of psoriatic plaques using deep CNN based multi-task learning,” in 2016 23rd International Conference on Pattern Recognition (ICPR), (IEEE, 2016), pp. 1478–1483.

Chefd’hotel, C.

T. Chen and C. Chefd’hotel, “Deep learning based automatic immune cell detection for immunohistochemistry images,” in Machine Learning in Medical Imaging, vol. 8679 (Springer, Cham, 2014), pp. 17–24.

Chen, J.

J. Chen, G. Bai, S. Liang, and Z. Li, “Automatic image cropping: A computational complexity study,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2016), pp. 507–515.

Chen, M.

V. O. Baron, M. Chen, S. O. Clark, A. Williams, R. J. H. Hammond, K. Dholakia, and S. H. Gillespie, “Label-free optical vibrational spectroscopy to detect the metabolic state of M. tuberculosis cells at the site of disease,” Sci. Rep. 7, 9844 (2017).
[Crossref] [PubMed]

N. McReynolds, F. G. M. Cooke, M. Chen, S. J. Powis, and K. Dholakia, “Multimodal discrimination of immune cells using a combination of Raman spectroscopy and digital holographic microscopy,” Sci. Reports 7, 43631 (2017).
[Crossref]

M. Chen, N. McReynolds, E. C. Campbell, M. Mazilu, J. Barbosa, K. Dholakia, and S. J. Powis, “The Use of Wavelength Modulated Raman Spectroscopy in Label-Free Identification of T Lymphocyte Subsets, Natural Killer Cells and Dendritic Cells,” Plos One 5, e0125158 (2015).
[Crossref]

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Y. J. Jo, S. Park, J. H. Jung, J. Yoon, H. Joo, M. H. Kim, S. J. Kang, M. C. Choi, S. Y. Lee, and Y. K. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3, e1700606 (2017).
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B. Microbiana, D. Hidalgo, M. Anthimopoilos, S. Christodoulidis, L. Ebner, A. Christe, and S. Mougiakakou, “Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network,” IEEE Transactions on Med. Imaging 35, 1207–1216 (2016).
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B. Microbiana, D. Hidalgo, M. Anthimopoilos, S. Christodoulidis, L. Ebner, A. Christe, and S. Mougiakakou, “Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network,” IEEE Transactions on Med. Imaging 35, 1207–1216 (2016).
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J. E. Repine, C. C. Clawson, and F. C. Goetz, “Bactericidal function of neutrophils from patients with acute bacterial infections and from diabetics,” The J. infectious diseases 142, 869–875 (1980).
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N. McReynolds, F. G. M. Cooke, M. Chen, S. J. Powis, and K. Dholakia, “Multimodal discrimination of immune cells using a combination of Raman spectroscopy and digital holographic microscopy,” Sci. Reports 7, 43631 (2017).
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J. R. MacKenzie, J. Mattes, L. A. Dent, and P. S. Foster, “Eosinophils promote allergic disease of the lung by regulating cd4+ th2 lymphocyte function,” The J. Immunol. 167, 3146–3155 (2001).
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N. McReynolds, F. G. M. Cooke, M. Chen, S. J. Powis, and K. Dholakia, “Multimodal discrimination of immune cells using a combination of Raman spectroscopy and digital holographic microscopy,” Sci. Reports 7, 43631 (2017).
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A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature 542, 115–118 (2017).
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M. J. Huttunen, A. Hassan, C. W. McCloskey, S. Fasih, J. Upham, B. C. Vanderhyden, R. W. Boyd, and S. Murugkar, “Automated classification of multiphoton microscopy images of ovarian tissue using deep learning,” J. Biomed. Opt. 23, 066002 (2018).
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A. Ramoji, U. Neugebauer, T. Bocklitz, M. Foerster, M. Kiehntopf, M. Bauer, and J. Popp, “Toward a spectroscopic hemogram: Raman spectroscopic differentiation of the two most abundant leukocytes from peripheral blood,” Anal. Chem. 84, 5335–5342 (2012).
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E. Griffin, L. Håkansson, H. Formgren, K. Jörgensen, C. Peterson, and P. Venge, “Blood eosinophil number and activity in relation to lung function in patients with asthma and with eosinophilia,” The J. allergy clinical immunology 87, 548–557 (1991).
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J. R. MacKenzie, J. Mattes, L. A. Dent, and P. S. Foster, “Eosinophils promote allergic disease of the lung by regulating cd4+ th2 lymphocyte function,” The J. Immunol. 167, 3146–3155 (2001).
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V. O. Baron, M. Chen, S. O. Clark, A. Williams, R. J. H. Hammond, K. Dholakia, and S. H. Gillespie, “Label-free optical vibrational spectroscopy to detect the metabolic state of M. tuberculosis cells at the site of disease,” Sci. Rep. 7, 9844 (2017).
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J. E. Repine, C. C. Clawson, and F. C. Goetz, “Bactericidal function of neutrophils from patients with acute bacterial infections and from diabetics,” The J. infectious diseases 142, 869–875 (1980).
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J. Arevalo, F. A. González, R. Ramos-Pollán, J. L. Oliveira, and M. A. Guevara Lopez, “Representation learning for mammography mass lesion classification with convolutional neural networks,” Comput. Methods Programs Biomed. 127, 248–257 (2016).
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A. C. De Luca, M. Mazilu, A. Riches, C. S. Herrington, and K. Dholakia, “Online fluorescence suppression in modulated raman spectroscopy,” Anal. Chem. 82, 738–745 (2010).
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B. Microbiana, D. Hidalgo, M. Anthimopoilos, S. Christodoulidis, L. Ebner, A. Christe, and S. Mougiakakou, “Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network,” IEEE Transactions on Med. Imaging 35, 1207–1216 (2016).
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Y. J. Jo, S. Park, J. H. Jung, J. Yoon, H. Joo, M. H. Kim, S. J. Kang, M. C. Choi, S. Y. Lee, and Y. K. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3, e1700606 (2017).
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Y. J. Jo, S. Park, J. H. Jung, J. Yoon, H. Joo, M. H. Kim, S. J. Kang, M. C. Choi, S. Y. Lee, and Y. K. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3, e1700606 (2017).
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A. Ramoji, U. Neugebauer, T. Bocklitz, M. Foerster, M. Kiehntopf, M. Bauer, and J. Popp, “Toward a spectroscopic hemogram: Raman spectroscopic differentiation of the two most abundant leukocytes from peripheral blood,” Anal. Chem. 84, 5335–5342 (2012).
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Y. J. Jo, S. Park, J. H. Jung, J. Yoon, H. Joo, M. H. Kim, S. J. Kang, M. C. Choi, S. Y. Lee, and Y. K. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3, e1700606 (2017).
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A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature 542, 115–118 (2017).
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X. Wang, W. Yang, J. Weinreb, J. Han, Q. Li, X. Kong, Y. Yan, Z. Ke, B. Luo, T. Liu, and L. Wang, “Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning,” Sci. Reports 7, 15415 (2017).
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P. Ruutu, T. Ruutu, P. Vuopio, T. U. Kosunen, and A. de la Chapelle, “Function of Neutrophils in Preleukaemia,” Scand. J. Haematol. 18, 317–325 (2009).
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Y. Lecun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521, 436–444 (2015).
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C. Fontanella, S. Bolzonello, B. Lederer, and G. Aprile, “Management of breast cancer patients with chemotherapy-induced neutropenia or febrile neutropenia,” Breast Care 9, 239–245 (2014).
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Y. J. Jo, S. Park, J. H. Jung, J. Yoon, H. Joo, M. H. Kim, S. J. Kang, M. C. Choi, S. Y. Lee, and Y. K. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3, e1700606 (2017).
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X. Wang, W. Yang, J. Weinreb, J. Han, Q. Li, X. Kong, Y. Yan, Z. Ke, B. Luo, T. Liu, and L. Wang, “Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning,” Sci. Reports 7, 15415 (2017).
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L. Wang, Y. Li, and S. Lazebnik, “Learning Deep Structure-Preserving Image-Text Embeddings,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (IEEE, 2016), pp. 5005–5013.

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X. Wang, W. Yang, J. Weinreb, J. Han, Q. Li, X. Kong, Y. Yan, Z. Ke, B. Luo, T. Liu, and L. Wang, “Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning,” Sci. Reports 7, 15415 (2017).
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F. Tacchini-Cottier, C. Zweifel, Y. Belkaid, C. Mukankundiye, M. Vasei, P. Launois, G. Milon, and J. A. Louis, “An immunomodulatory function for neutrophils during the induction of a CD4+ Th2 response in BALB/c mice infected with Leishmania major,” The J. Immunol. 165, 2628–2636 (2000).
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A. C. De Luca, M. Mazilu, A. Riches, C. S. Herrington, and K. Dholakia, “Online fluorescence suppression in modulated raman spectroscopy,” Anal. Chem. 82, 738–745 (2010).
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Luo, B.

X. Wang, W. Yang, J. Weinreb, J. Han, Q. Li, X. Kong, Y. Yan, Z. Ke, B. Luo, T. Liu, and L. Wang, “Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning,” Sci. Reports 7, 15415 (2017).
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J. R. MacKenzie, J. Mattes, L. A. Dent, and P. S. Foster, “Eosinophils promote allergic disease of the lung by regulating cd4+ th2 lymphocyte function,” The J. Immunol. 167, 3146–3155 (2001).
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J. R. MacKenzie, J. Mattes, L. A. Dent, and P. S. Foster, “Eosinophils promote allergic disease of the lung by regulating cd4+ th2 lymphocyte function,” The J. Immunol. 167, 3146–3155 (2001).
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Mazilu, M.

M. Chen, N. McReynolds, E. C. Campbell, M. Mazilu, J. Barbosa, K. Dholakia, and S. J. Powis, “The Use of Wavelength Modulated Raman Spectroscopy in Label-Free Identification of T Lymphocyte Subsets, Natural Killer Cells and Dendritic Cells,” Plos One 5, e0125158 (2015).
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A. C. De Luca, M. Mazilu, A. Riches, C. S. Herrington, and K. Dholakia, “Online fluorescence suppression in modulated raman spectroscopy,” Anal. Chem. 82, 738–745 (2010).
[Crossref]

McCloskey, C. W.

M. J. Huttunen, A. Hassan, C. W. McCloskey, S. Fasih, J. Upham, B. C. Vanderhyden, R. W. Boyd, and S. Murugkar, “Automated classification of multiphoton microscopy images of ovarian tissue using deep learning,” J. Biomed. Opt. 23, 066002 (2018).
[Crossref]

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N. McReynolds, F. G. M. Cooke, M. Chen, S. J. Powis, and K. Dholakia, “Multimodal discrimination of immune cells using a combination of Raman spectroscopy and digital holographic microscopy,” Sci. Reports 7, 43631 (2017).
[Crossref]

M. Chen, N. McReynolds, E. C. Campbell, M. Mazilu, J. Barbosa, K. Dholakia, and S. J. Powis, “The Use of Wavelength Modulated Raman Spectroscopy in Label-Free Identification of T Lymphocyte Subsets, Natural Killer Cells and Dendritic Cells,” Plos One 5, e0125158 (2015).
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B. Microbiana, D. Hidalgo, M. Anthimopoilos, S. Christodoulidis, L. Ebner, A. Christe, and S. Mougiakakou, “Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network,” IEEE Transactions on Med. Imaging 35, 1207–1216 (2016).
[Crossref]

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F. Tacchini-Cottier, C. Zweifel, Y. Belkaid, C. Mukankundiye, M. Vasei, P. Launois, G. Milon, and J. A. Louis, “An immunomodulatory function for neutrophils during the induction of a CD4+ Th2 response in BALB/c mice infected with Leishmania major,” The J. Immunol. 165, 2628–2636 (2000).
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B. Microbiana, D. Hidalgo, M. Anthimopoilos, S. Christodoulidis, L. Ebner, A. Christe, and S. Mougiakakou, “Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network,” IEEE Transactions on Med. Imaging 35, 1207–1216 (2016).
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F. Tacchini-Cottier, C. Zweifel, Y. Belkaid, C. Mukankundiye, M. Vasei, P. Launois, G. Milon, and J. A. Louis, “An immunomodulatory function for neutrophils during the induction of a CD4+ Th2 response in BALB/c mice infected with Leishmania major,” The J. Immunol. 165, 2628–2636 (2000).
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M. J. Huttunen, A. Hassan, C. W. McCloskey, S. Fasih, J. Upham, B. C. Vanderhyden, R. W. Boyd, and S. Murugkar, “Automated classification of multiphoton microscopy images of ovarian tissue using deep learning,” J. Biomed. Opt. 23, 066002 (2018).
[Crossref]

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A. Ramoji, U. Neugebauer, T. Bocklitz, M. Foerster, M. Kiehntopf, M. Bauer, and J. Popp, “Toward a spectroscopic hemogram: Raman spectroscopic differentiation of the two most abundant leukocytes from peripheral blood,” Anal. Chem. 84, 5335–5342 (2012).
[Crossref] [PubMed]

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A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature 542, 115–118 (2017).
[Crossref] [PubMed]

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A. D. Klion and T. B. Nutman, “The role of eosinophils in host defense against helminth parasites,” J. Allergy Clin. Immunol. 113, 30–37 (2004).
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J. Arevalo, F. A. González, R. Ramos-Pollán, J. L. Oliveira, and M. A. Guevara Lopez, “Representation learning for mammography mass lesion classification with convolutional neural networks,” Comput. Methods Programs Biomed. 127, 248–257 (2016).
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A. Pal, A. Chaturvedi, U. Garain, A. Chandra, and R. Chatterjee, “Severity grading of psoriatic plaques using deep CNN based multi-task learning,” in 2016 23rd International Conference on Pattern Recognition (ICPR), (IEEE, 2016), pp. 1478–1483.

Parham, P.

P. Parham, The Immune System, 4th ed. (Garland Science, 2015).

Park, S.

Y. J. Jo, S. Park, J. H. Jung, J. Yoon, H. Joo, M. H. Kim, S. J. Kang, M. C. Choi, S. Y. Lee, and Y. K. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3, e1700606 (2017).
[Crossref] [PubMed]

Park, Y. K.

Y. J. Jo, S. Park, J. H. Jung, J. Yoon, H. Joo, M. H. Kim, S. J. Kang, M. C. Choi, S. Y. Lee, and Y. K. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3, e1700606 (2017).
[Crossref] [PubMed]

Pascual, R. M.

R. M. Pascual and S. P. Peters, “Airway remodeling contributes to the progressive loss of lung function in asthma: an overview,” The J. allergy clinical immunology 116, 477–486 (2005).
[Crossref]

Peng, C.

H. Yan, Y. Jiang, J. Zheng, C. Peng, and Q. Li, “A multilayer perceptron-based medical decision support system for heart disease diagnosis,” Expert Syst. with Appl. 30, 272–281 (2006).
[Crossref]

Peters, S. P.

R. M. Pascual and S. P. Peters, “Airway remodeling contributes to the progressive loss of lung function in asthma: an overview,” The J. allergy clinical immunology 116, 477–486 (2005).
[Crossref]

Peterson, C.

E. Griffin, L. Håkansson, H. Formgren, K. Jörgensen, C. Peterson, and P. Venge, “Blood eosinophil number and activity in relation to lung function in patients with asthma and with eosinophilia,” The J. allergy clinical immunology 87, 548–557 (1991).
[Crossref]

Popp, J.

A. Ramoji, U. Neugebauer, T. Bocklitz, M. Foerster, M. Kiehntopf, M. Bauer, and J. Popp, “Toward a spectroscopic hemogram: Raman spectroscopic differentiation of the two most abundant leukocytes from peripheral blood,” Anal. Chem. 84, 5335–5342 (2012).
[Crossref] [PubMed]

Powis, S. J.

N. McReynolds, F. G. M. Cooke, M. Chen, S. J. Powis, and K. Dholakia, “Multimodal discrimination of immune cells using a combination of Raman spectroscopy and digital holographic microscopy,” Sci. Reports 7, 43631 (2017).
[Crossref]

M. Chen, N. McReynolds, E. C. Campbell, M. Mazilu, J. Barbosa, K. Dholakia, and S. J. Powis, “The Use of Wavelength Modulated Raman Spectroscopy in Label-Free Identification of T Lymphocyte Subsets, Natural Killer Cells and Dendritic Cells,” Plos One 5, e0125158 (2015).
[Crossref]

Prasad, A.

S. M. Lyons, E. Alizadeh, J. Mannheimer, K. Schuamberg, J. Castle, B. Schroder, P. Turk, D. Thamm, and A. Prasad, “Changes in cell shape are correlated with metastatic potential in murine and human osteosarcomas,” Biol. Open 5, 289–299 (2016).
[Crossref] [PubMed]

Prasad, S.

S. Prasad and L. M. Bruce, “Limitations of Principal Components Analysis for Hyperspectral Target Recognition,” Geosci. Remote Sens. Lett., IEEE 5, 625–629 (2008).
[Crossref]

Ramoji, A.

A. Ramoji, U. Neugebauer, T. Bocklitz, M. Foerster, M. Kiehntopf, M. Bauer, and J. Popp, “Toward a spectroscopic hemogram: Raman spectroscopic differentiation of the two most abundant leukocytes from peripheral blood,” Anal. Chem. 84, 5335–5342 (2012).
[Crossref] [PubMed]

Ramos-Pollán, R.

J. Arevalo, F. A. González, R. Ramos-Pollán, J. L. Oliveira, and M. A. Guevara Lopez, “Representation learning for mammography mass lesion classification with convolutional neural networks,” Comput. Methods Programs Biomed. 127, 248–257 (2016).
[Crossref] [PubMed]

Repine, J. E.

J. E. Repine, C. C. Clawson, and F. C. Goetz, “Bactericidal function of neutrophils from patients with acute bacterial infections and from diabetics,” The J. infectious diseases 142, 869–875 (1980).
[Crossref]

Riches, A.

A. C. De Luca, M. Mazilu, A. Riches, C. S. Herrington, and K. Dholakia, “Online fluorescence suppression in modulated raman spectroscopy,” Anal. Chem. 82, 738–745 (2010).
[Crossref]

Rosenberg, H. F.

H. F. Rosenberg and J. B. Domachowske, “Eosinophils, eosinophil ribonucleases, and their role in host defense against respiratory virus pathogens,” J. Leukoc. Biol. 70, 691–698 (2001).
[PubMed]

Ruutu, P.

P. Ruutu, T. Ruutu, P. Vuopio, T. U. Kosunen, and A. de la Chapelle, “Function of Neutrophils in Preleukaemia,” Scand. J. Haematol. 18, 317–325 (2009).
[Crossref]

Ruutu, T.

P. Ruutu, T. Ruutu, P. Vuopio, T. U. Kosunen, and A. de la Chapelle, “Function of Neutrophils in Preleukaemia,” Scand. J. Haematol. 18, 317–325 (2009).
[Crossref]

Schroder, B.

S. M. Lyons, E. Alizadeh, J. Mannheimer, K. Schuamberg, J. Castle, B. Schroder, P. Turk, D. Thamm, and A. Prasad, “Changes in cell shape are correlated with metastatic potential in murine and human osteosarcomas,” Biol. Open 5, 289–299 (2016).
[Crossref] [PubMed]

Schroder, K.

C. J. Thomas and K. Schroder, “Pattern recognition receptor function in neutrophils,” Trends Immunol. 34, 317–328 (2013).
[Crossref] [PubMed]

Schuamberg, K.

S. M. Lyons, E. Alizadeh, J. Mannheimer, K. Schuamberg, J. Castle, B. Schroder, P. Turk, D. Thamm, and A. Prasad, “Changes in cell shape are correlated with metastatic potential in murine and human osteosarcomas,” Biol. Open 5, 289–299 (2016).
[Crossref] [PubMed]

Standiford, T. J.

M. A. Kovach and T. J. Standiford, “The function of neutrophils in sepsis,” Curr. Opin. Infect. Dis. 25, 321–327 (2012).
[Crossref] [PubMed]

Suganthan, P.

L. Zhang and P. Suganthan, “A survey of randomized algorithms for training neural networks,” Inf. Sci. 364-365, 146–155 (2016).
[Crossref]

Swetter, S. M.

A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature 542, 115–118 (2017).
[Crossref] [PubMed]

Szegedy, C.

S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” Proc. 32nd Int. Conf. on Mach. Learn. Lille, France37, 448–456 (2015).

Tacchini-Cottier, F.

F. Tacchini-Cottier, C. Zweifel, Y. Belkaid, C. Mukankundiye, M. Vasei, P. Launois, G. Milon, and J. A. Louis, “An immunomodulatory function for neutrophils during the induction of a CD4+ Th2 response in BALB/c mice infected with Leishmania major,” The J. Immunol. 165, 2628–2636 (2000).
[Crossref] [PubMed]

Talbot, N. L.

G. C. Cawley and N. L. Talbot, “On over-fitting in model selection and subsequent selection bias in performance evaluation,” J. Mach. Learn. Res. 11, 2079–2107 (2010).

Thamm, D.

S. M. Lyons, E. Alizadeh, J. Mannheimer, K. Schuamberg, J. Castle, B. Schroder, P. Turk, D. Thamm, and A. Prasad, “Changes in cell shape are correlated with metastatic potential in murine and human osteosarcomas,” Biol. Open 5, 289–299 (2016).
[Crossref] [PubMed]

Thomas, C. J.

C. J. Thomas and K. Schroder, “Pattern recognition receptor function in neutrophils,” Trends Immunol. 34, 317–328 (2013).
[Crossref] [PubMed]

Thrun, S.

A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature 542, 115–118 (2017).
[Crossref] [PubMed]

Turk, P.

S. M. Lyons, E. Alizadeh, J. Mannheimer, K. Schuamberg, J. Castle, B. Schroder, P. Turk, D. Thamm, and A. Prasad, “Changes in cell shape are correlated with metastatic potential in murine and human osteosarcomas,” Biol. Open 5, 289–299 (2016).
[Crossref] [PubMed]

Upham, J.

M. J. Huttunen, A. Hassan, C. W. McCloskey, S. Fasih, J. Upham, B. C. Vanderhyden, R. W. Boyd, and S. Murugkar, “Automated classification of multiphoton microscopy images of ovarian tissue using deep learning,” J. Biomed. Opt. 23, 066002 (2018).
[Crossref]

Vanderhyden, B. C.

M. J. Huttunen, A. Hassan, C. W. McCloskey, S. Fasih, J. Upham, B. C. Vanderhyden, R. W. Boyd, and S. Murugkar, “Automated classification of multiphoton microscopy images of ovarian tissue using deep learning,” J. Biomed. Opt. 23, 066002 (2018).
[Crossref]

Vasei, M.

F. Tacchini-Cottier, C. Zweifel, Y. Belkaid, C. Mukankundiye, M. Vasei, P. Launois, G. Milon, and J. A. Louis, “An immunomodulatory function for neutrophils during the induction of a CD4+ Th2 response in BALB/c mice infected with Leishmania major,” The J. Immunol. 165, 2628–2636 (2000).
[Crossref] [PubMed]

Venge, P.

E. Griffin, L. Håkansson, H. Formgren, K. Jörgensen, C. Peterson, and P. Venge, “Blood eosinophil number and activity in relation to lung function in patients with asthma and with eosinophilia,” The J. allergy clinical immunology 87, 548–557 (1991).
[Crossref]

Vuopio, P.

P. Ruutu, T. Ruutu, P. Vuopio, T. U. Kosunen, and A. de la Chapelle, “Function of Neutrophils in Preleukaemia,” Scand. J. Haematol. 18, 317–325 (2009).
[Crossref]

Wang, L.

X. Wang, W. Yang, J. Weinreb, J. Han, Q. Li, X. Kong, Y. Yan, Z. Ke, B. Luo, T. Liu, and L. Wang, “Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning,” Sci. Reports 7, 15415 (2017).
[Crossref]

L. Wang, Y. Li, and S. Lazebnik, “Learning Deep Structure-Preserving Image-Text Embeddings,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (IEEE, 2016), pp. 5005–5013.

Wang, X.

X. Wang, W. Yang, J. Weinreb, J. Han, Q. Li, X. Kong, Y. Yan, Z. Ke, B. Luo, T. Liu, and L. Wang, “Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning,” Sci. Reports 7, 15415 (2017).
[Crossref]

Weinreb, J.

X. Wang, W. Yang, J. Weinreb, J. Han, Q. Li, X. Kong, Y. Yan, Z. Ke, B. Luo, T. Liu, and L. Wang, “Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning,” Sci. Reports 7, 15415 (2017).
[Crossref]

Williams, A.

V. O. Baron, M. Chen, S. O. Clark, A. Williams, R. J. H. Hammond, K. Dholakia, and S. H. Gillespie, “Label-free optical vibrational spectroscopy to detect the metabolic state of M. tuberculosis cells at the site of disease,” Sci. Rep. 7, 9844 (2017).
[Crossref] [PubMed]

V. O. Baron, M. Chen, S. O. Clark, A. Williams, K. Dholakia, and S. H. Gillespie, Detecting Phenotypically Resistant Mycobacterium tuberculosis Using Wavelength Modulated Raman Spectroscopy(SpringerNew York, 2018), pp. 41–50.

Yan, H.

H. Yan, Y. Jiang, J. Zheng, C. Peng, and Q. Li, “A multilayer perceptron-based medical decision support system for heart disease diagnosis,” Expert Syst. with Appl. 30, 272–281 (2006).
[Crossref]

Yan, Y.

X. Wang, W. Yang, J. Weinreb, J. Han, Q. Li, X. Kong, Y. Yan, Z. Ke, B. Luo, T. Liu, and L. Wang, “Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning,” Sci. Reports 7, 15415 (2017).
[Crossref]

Yang, W.

X. Wang, W. Yang, J. Weinreb, J. Han, Q. Li, X. Kong, Y. Yan, Z. Ke, B. Luo, T. Liu, and L. Wang, “Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning,” Sci. Reports 7, 15415 (2017).
[Crossref]

Yoon, J.

Y. J. Jo, S. Park, J. H. Jung, J. Yoon, H. Joo, M. H. Kim, S. J. Kang, M. C. Choi, S. Y. Lee, and Y. K. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3, e1700606 (2017).
[Crossref] [PubMed]

Zeiler, M. D.

M. D. Zeiler, “ADADELTA: an adaptive learning rate method,” CoRR abs/1212.5701 (2012).

Zhang, L.

L. Zhang and P. Suganthan, “A survey of randomized algorithms for training neural networks,” Inf. Sci. 364-365, 146–155 (2016).
[Crossref]

Zheng, J.

H. Yan, Y. Jiang, J. Zheng, C. Peng, and Q. Li, “A multilayer perceptron-based medical decision support system for heart disease diagnosis,” Expert Syst. with Appl. 30, 272–281 (2006).
[Crossref]

Zweifel, C.

F. Tacchini-Cottier, C. Zweifel, Y. Belkaid, C. Mukankundiye, M. Vasei, P. Launois, G. Milon, and J. A. Louis, “An immunomodulatory function for neutrophils during the induction of a CD4+ Th2 response in BALB/c mice infected with Leishmania major,” The J. Immunol. 165, 2628–2636 (2000).
[Crossref] [PubMed]

Anal. Chem. (2)

A. Ramoji, U. Neugebauer, T. Bocklitz, M. Foerster, M. Kiehntopf, M. Bauer, and J. Popp, “Toward a spectroscopic hemogram: Raman spectroscopic differentiation of the two most abundant leukocytes from peripheral blood,” Anal. Chem. 84, 5335–5342 (2012).
[Crossref] [PubMed]

A. C. De Luca, M. Mazilu, A. Riches, C. S. Herrington, and K. Dholakia, “Online fluorescence suppression in modulated raman spectroscopy,” Anal. Chem. 82, 738–745 (2010).
[Crossref]

Biol. Open (1)

S. M. Lyons, E. Alizadeh, J. Mannheimer, K. Schuamberg, J. Castle, B. Schroder, P. Turk, D. Thamm, and A. Prasad, “Changes in cell shape are correlated with metastatic potential in murine and human osteosarcomas,” Biol. Open 5, 289–299 (2016).
[Crossref] [PubMed]

Breast Care (1)

C. Fontanella, S. Bolzonello, B. Lederer, and G. Aprile, “Management of breast cancer patients with chemotherapy-induced neutropenia or febrile neutropenia,” Breast Care 9, 239–245 (2014).
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Comput. Methods Programs Biomed. (1)

J. Arevalo, F. A. González, R. Ramos-Pollán, J. L. Oliveira, and M. A. Guevara Lopez, “Representation learning for mammography mass lesion classification with convolutional neural networks,” Comput. Methods Programs Biomed. 127, 248–257 (2016).
[Crossref] [PubMed]

Curr. Opin. Immunol. (1)

D. Dombrowicz and M. Capron, “Eosinophils, allergy and parasites,” Curr. Opin. Immunol. 13, 716–720 (2001).
[Crossref] [PubMed]

Curr. Opin. Infect. Dis. (1)

M. A. Kovach and T. J. Standiford, “The function of neutrophils in sepsis,” Curr. Opin. Infect. Dis. 25, 321–327 (2012).
[Crossref] [PubMed]

Expert Syst. with Appl. (2)

H. Yan, Y. Jiang, J. Zheng, C. Peng, and Q. Li, “A multilayer perceptron-based medical decision support system for heart disease diagnosis,” Expert Syst. with Appl. 30, 272–281 (2006).
[Crossref]

U. Orhan, M. Hekim, and M. Ozer, “Eeg signals classification using the k-means clustering and a multilayer perceptron neural network model,” Expert Syst. with Appl. 38, 13475–13481 (2011).
[Crossref]

Geosci. Remote Sens. Lett., IEEE (1)

S. Prasad and L. M. Bruce, “Limitations of Principal Components Analysis for Hyperspectral Target Recognition,” Geosci. Remote Sens. Lett., IEEE 5, 625–629 (2008).
[Crossref]

IEEE Transactions on Med. Imaging (1)

B. Microbiana, D. Hidalgo, M. Anthimopoilos, S. Christodoulidis, L. Ebner, A. Christe, and S. Mougiakakou, “Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network,” IEEE Transactions on Med. Imaging 35, 1207–1216 (2016).
[Crossref]

Inf. Sci. (1)

L. Zhang and P. Suganthan, “A survey of randomized algorithms for training neural networks,” Inf. Sci. 364-365, 146–155 (2016).
[Crossref]

Int. J. Comput. Appl. (1)

N. Ganesan, “Application of neural networks in diagnosing cancer disease using demographic data,” Int. J. Comput. Appl. ( 09751, 76–85 (2010).

J. Allergy Clin. Immunol. (1)

A. D. Klion and T. B. Nutman, “The role of eosinophils in host defense against helminth parasites,” J. Allergy Clin. Immunol. 113, 30–37 (2004).
[Crossref] [PubMed]

J. Biomed. Opt. (1)

M. J. Huttunen, A. Hassan, C. W. McCloskey, S. Fasih, J. Upham, B. C. Vanderhyden, R. W. Boyd, and S. Murugkar, “Automated classification of multiphoton microscopy images of ovarian tissue using deep learning,” J. Biomed. Opt. 23, 066002 (2018).
[Crossref]

J. Leukoc. Biol. (1)

H. F. Rosenberg and J. B. Domachowske, “Eosinophils, eosinophil ribonucleases, and their role in host defense against respiratory virus pathogens,” J. Leukoc. Biol. 70, 691–698 (2001).
[PubMed]

J. Mach. Learn. Res. (1)

G. C. Cawley and N. L. Talbot, “On over-fitting in model selection and subsequent selection bias in performance evaluation,” J. Mach. Learn. Res. 11, 2079–2107 (2010).

Memórias do Instituto Oswaldo Cruz (1)

M. Capron and M. Capron, “Dual function of eosinophils in pathogenesis and protective immunity against parasites,” Memórias do Instituto Oswaldo Cruz 87, 83–89 (1992).
[Crossref]

Nature (2)

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

A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature 542, 115–118 (2017).
[Crossref] [PubMed]

Plos One (1)

M. Chen, N. McReynolds, E. C. Campbell, M. Mazilu, J. Barbosa, K. Dholakia, and S. J. Powis, “The Use of Wavelength Modulated Raman Spectroscopy in Label-Free Identification of T Lymphocyte Subsets, Natural Killer Cells and Dendritic Cells,” Plos One 5, e0125158 (2015).
[Crossref]

Scand. J. Haematol. (1)

P. Ruutu, T. Ruutu, P. Vuopio, T. U. Kosunen, and A. de la Chapelle, “Function of Neutrophils in Preleukaemia,” Scand. J. Haematol. 18, 317–325 (2009).
[Crossref]

Sci. Adv. (1)

Y. J. Jo, S. Park, J. H. Jung, J. Yoon, H. Joo, M. H. Kim, S. J. Kang, M. C. Choi, S. Y. Lee, and Y. K. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3, e1700606 (2017).
[Crossref] [PubMed]

Sci. Rep. (1)

V. O. Baron, M. Chen, S. O. Clark, A. Williams, R. J. H. Hammond, K. Dholakia, and S. H. Gillespie, “Label-free optical vibrational spectroscopy to detect the metabolic state of M. tuberculosis cells at the site of disease,” Sci. Rep. 7, 9844 (2017).
[Crossref] [PubMed]

Sci. Reports (2)

X. Wang, W. Yang, J. Weinreb, J. Han, Q. Li, X. Kong, Y. Yan, Z. Ke, B. Luo, T. Liu, and L. Wang, “Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning,” Sci. Reports 7, 15415 (2017).
[Crossref]

N. McReynolds, F. G. M. Cooke, M. Chen, S. J. Powis, and K. Dholakia, “Multimodal discrimination of immune cells using a combination of Raman spectroscopy and digital holographic microscopy,” Sci. Reports 7, 43631 (2017).
[Crossref]

The J. allergy clinical immunology (2)

E. Griffin, L. Håkansson, H. Formgren, K. Jörgensen, C. Peterson, and P. Venge, “Blood eosinophil number and activity in relation to lung function in patients with asthma and with eosinophilia,” The J. allergy clinical immunology 87, 548–557 (1991).
[Crossref]

R. M. Pascual and S. P. Peters, “Airway remodeling contributes to the progressive loss of lung function in asthma: an overview,” The J. allergy clinical immunology 116, 477–486 (2005).
[Crossref]

The J. Immunol. (2)

J. R. MacKenzie, J. Mattes, L. A. Dent, and P. S. Foster, “Eosinophils promote allergic disease of the lung by regulating cd4+ th2 lymphocyte function,” The J. Immunol. 167, 3146–3155 (2001).
[Crossref]

F. Tacchini-Cottier, C. Zweifel, Y. Belkaid, C. Mukankundiye, M. Vasei, P. Launois, G. Milon, and J. A. Louis, “An immunomodulatory function for neutrophils during the induction of a CD4+ Th2 response in BALB/c mice infected with Leishmania major,” The J. Immunol. 165, 2628–2636 (2000).
[Crossref] [PubMed]

The J. infectious diseases (1)

J. E. Repine, C. C. Clawson, and F. C. Goetz, “Bactericidal function of neutrophils from patients with acute bacterial infections and from diabetics,” The J. infectious diseases 142, 869–875 (1980).
[Crossref]

Trends Immunol. (1)

C. J. Thomas and K. Schroder, “Pattern recognition receptor function in neutrophils,” Trends Immunol. 34, 317–328 (2013).
[Crossref] [PubMed]

Other (8)

P. Parham, The Immune System, 4th ed. (Garland Science, 2015).

A. Pal, A. Chaturvedi, U. Garain, A. Chandra, and R. Chatterjee, “Severity grading of psoriatic plaques using deep CNN based multi-task learning,” in 2016 23rd International Conference on Pattern Recognition (ICPR), (IEEE, 2016), pp. 1478–1483.

T. Chen and C. Chefd’hotel, “Deep learning based automatic immune cell detection for immunohistochemistry images,” in Machine Learning in Medical Imaging, vol. 8679 (Springer, Cham, 2014), pp. 17–24.

V. O. Baron, M. Chen, S. O. Clark, A. Williams, K. Dholakia, and S. H. Gillespie, Detecting Phenotypically Resistant Mycobacterium tuberculosis Using Wavelength Modulated Raman Spectroscopy(SpringerNew York, 2018), pp. 41–50.

S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” Proc. 32nd Int. Conf. on Mach. Learn. Lille, France37, 448–456 (2015).

M. D. Zeiler, “ADADELTA: an adaptive learning rate method,” CoRR abs/1212.5701 (2012).

J. Chen, G. Bai, S. Liang, and Z. Li, “Automatic image cropping: A computational complexity study,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2016), pp. 507–515.

L. Wang, Y. Li, and S. Lazebnik, “Learning Deep Structure-Preserving Image-Text Embeddings,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (IEEE, 2016), pp. 5005–5013.

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Figures (9)

Fig. 1
Fig. 1 Schematic of the Convolutional Neural Network. The network takes phase images as its input and processes these though four convolution blocks and finally connects to a sigmoid neuron for the binary classification. Here we see the (a) Input phaseimage, (b) the convolution layer containing 30 5×5 filters with 1×1 stride and 1×1 padding, and (c) the convolution layer containing 30 4×4 filters with 1×1 stride and 1×1 padding, (d) the convolution layer containing 30 3×3 filters with 1×1 stride and 1×1 padding,(e) the convolution layer with 30 2×2 filters with 5×5 stride and 1×1 padding. Each convolution layer is followed by a batch normalization and ReLu Layer. The final convolution block has a max-pooling layer with 2×2 filter size and 1×1 padding and (f) shows the binary representation of output classification layer with a single sigmoid neuron which is fully connected to the output of the final convolution block.
Fig. 2
Fig. 2 Schematic of the Multi-layered perceptron. The network takes a WMR spectrum as the input (layer with 351 neurons) and processes it though a hidden layer (90 neurons) and finally connects to a sigmoid neuron for the binary classification. (a) Representation of mean WMR spectra for the two cell lines, (b) input Layer with nodes equal to the number of data points in each spectrum,(c) hidden layer with 90 neurons and tan hyperbolic as the activation function, (d) representation of classification layer with a single logistic sigmoid neuron for binary classification, here red neuron represents the Eosinophil class whereas the green neuron represents the Neutrophil class.
Fig. 3
Fig. 3 Flow cytometric analysis of purified untouched eosinophils (a) and neutrophils (b) demonstrating forward and side scatter profiles of purified cells and antibody staining with anti-CD3-FITC (negative control) and anti-CD66b-FITC for eosinophils,and anti-CD3-FITC (negative control) and anti-CD15-FITC for neutrophils.
Fig. 4
Fig. 4 (a) A subsection of the accumulated bright field image using our microscope, (b) fringe image for the same section, (c) phase image extracted from (b). Scale bars: 10 μm (image), 5 μm (inset).
Fig. 5
Fig. 5 Normalized phase images of the Granulocytes. (a), (b), (c) Neutrophils; (d),(e),(f) Eosinophils. These images represent the inter-cellular structural variation in the form of refractive index map which expresses the granularity of the two cell types. Color bar represents the normalized phase difference between the signal and reference arm.
Fig. 6
Fig. 6 (a) PC1-PC2 scatter plot for the two cell types; (b) PC2-PC3 scatter plot for the two cell types; (c) PC3-PC1 scatter plot for the two cell types; Eosinophils are represented by the data points in red whereas the neutrophils are represented by the data points in blue
Fig. 7
Fig. 7 Wavelength modulated Raman spectra illustrating pairwise comparison between eosinophils and neutrophils. Solid lines show the mean spectrum for each cell subset and shadowed regions represent the standard deviation.
Fig. 8
Fig. 8 Principal component analysis and t-SNE scatter plots. (a) 3D PC scatter plot showing the clustering of the neutrophils and eosinophils. The red points on the scatter plot correspond to the Eosinophil WMR spectra whereas the green points on the scatter plot correspond to the neutrophil WMR spectra. (b) t-SNE scatter plot in 2D showing a clear clustering of the complete dataset. The red points in the scatter plot correspond to Eosinophils WMR spectra whereas blue points correspond to Neutrophil WMR spectra.
Fig. 9
Fig. 9 Demonstration of the automated feature detection and classification using a CNN. (a) Input phase image of an Eosinophil from test dataset. Different column images represent five of the total layer activations, here (b), (c), (d) and (e) represent the network activations at the four convolution layers.

Tables (10)

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Table 1 Summary of DHM wide field phase images considered for cropping the individual phase images.

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Table 2 Summary of the single cell DHM phase image dataset collected from the three donors

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Table 3 Confusion matrix summarizing the classification performance of the CNN over the training set of the phase images

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Table 4 Confusion matrix summarizing the classification performance of the CNN over the validation set of phase images

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Table 5 Confusion matrix representing the prediction accuracy of the trained CNN and PCA/LOOCV for classification of the neutrophils and eosinophils on the test dataset. Each row of the matrix expresses the total number of phase images of the cells available for classification, whereas each column represents the predicted cell lines. Thus, the diagonal elements of the confusion matrix represent the correct predictions made by the network whereas off-diagonal terms represent the inaccurate predictions.

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Table 6 Confusion matrix summarizing the classification performance of the PCA over the training and validation set of phase images

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Table 7 Confusion matrix summarizing the performance of MLP on the training dataset

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Table 8 Confusion matrix summarizing the performance of MLP on the validation dataset

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Table 9 Confusion matrix summarizing the performance of PCA on the Training and validation datasets for WMRS

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Table 10 Confusion matrix representing the prediction accuracy of the trained MLP and PCA/LOOCV for classification of the neutrophils and eosinophils on the test dataset. Each row of the matrix expresses the total number of WMRS of the cells available for classification, whereas each column represents the predicted cell lines. Thus, the diagonal elements of the confusion matrix represent the correct predictions made by the network whereas off-diagonal terms represent the inaccurate predictions.

Equations (1)

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L ( j , k ) = { A 1 k l o g ( p ( k | j ) ) , for k = 1 A 2 ( 1 k ) l o g ( p ( k | j ) ) , for k = 0