Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study. Faes, L., Wagner, S. K, Fu, D. J., Liu, X., Korot, E., Ledsam, J. R, Back, T., Chopra, R., Pontikos, N., Kern, C., Moraes, G., Schmid, M. K, Sim, D., Balaskas, K., Bachmann, L. M, Denniston, A. K, & Keane, P. A The Lancet Digital Health, 1(5):e232–e242, September, 2019.
Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study [link]Paper  doi  bibtex   
@article{faes_automated_2019,
	title = {Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study},
	volume = {1},
	issn = {25897500},
	shorttitle = {Automated deep learning design for medical image classification by health-care professionals with no coding experience},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S2589750019301086},
	doi = {10.1016/S2589-7500(19)30108-6},
	language = {en},
	number = {5},
	urldate = {2021-06-16},
	journal = {The Lancet Digital Health},
	author = {Faes, Livia and Wagner, Siegfried K and Fu, Dun Jack and Liu, Xiaoxuan and Korot, Edward and Ledsam, Joseph R and Back, Trevor and Chopra, Reena and Pontikos, Nikolas and Kern, Christoph and Moraes, Gabriella and Schmid, Martin K and Sim, Dawn and Balaskas, Konstantinos and Bachmann, Lucas M and Denniston, Alastair K and Keane, Pearse A},
	month = sep,
	year = {2019},
	pages = {e232--e242},
}

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