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The promise and challenges of deep learning models for automated histopathologic classification and mutation prediction in lung cancer

  
@article{JTD25936,
	author = {Pradnya D. Patil and Brian Hobbs and Nathan A. Pennell},
	title = {The promise and challenges of deep learning models for automated histopathologic classification and mutation prediction in lung cancer},
	journal = {Journal of Thoracic Disease},
	volume = {11},
	number = {2},
	year = {2018},
	keywords = {},
	abstract = {The histologic subclassification of tumors as either squamous cell carcinoma or adenocarcinoma is used by convention to guide systemic chemotherapy for patients diagnosed with non-small cell lung cancer (NSCLC) (1,2). The current gold standard for histopathologic classification of tissue specimens is visual microscopic inspection of tissue specimens by pathologists.},
	issn = {2077-6624},	url = {http://jtd.amegroups.com/article/view/25936}
}