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Comment
. 2021 May-Jun;10(3):335-336.
doi: 10.1097/APO.0000000000000398.

Revisiting the Problem of Optic Nerve Detection in a Retinal Image Using Automated Machine Learning

Fares Antaki  1   2   3 , Razek Georges Coussa  4 , Karim Hammamji  1   2 , Renaud Duval  1   3
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Free article
Comment

Revisiting the Problem of Optic Nerve Detection in a Retinal Image Using Automated Machine Learning

Fares Antaki et al. Asia Pac J Ophthalmol (Phila). 2021 May-Jun.
Free article
No abstract available

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Conflict of interest statement

The authors have no funding or conflicts of interest to declare.

Comment in

Comment on

  • Big Data in Ophthalmology.
    Cheng CY, Soh ZD, Majithia S, Thakur S, Rim TH, Tham YC, Wong TY. Cheng CY, et al. Asia Pac J Ophthalmol (Phila). 2020 Jul-Aug;9(4):291-298. doi: 10.1097/APO.0000000000000304. Asia Pac J Ophthalmol (Phila). 2020. PMID: 32739936 Review.

References

    1. Cheng CY, Soh ZD, Majithia S, et al. Big Data in Ophthalmology. Asia Pac J Ophthalmol (Phila) 2020; 9:291–298.
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    1. Faes L, Wagner SK, Fu DJ, et al. Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study. Lancet Digit Health 2019; 1:e232–e242.
    1. Antaki F, Kahwati G, Sebag J, et al. Predictive modeling of proliferative vitreoretinopathy using automated machine learning by ophthalmologists without coding experience. Sci Rep 2020; 10:19528.
    1. Hoover A, Goldbaum M. Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels. IEEE Trans Med Imaging 2003; 22:951–958.