@incollection{,
	
					
					
					00A8CC5A0B651F4898D248B5C8AE7924
				,
	author={{DominikLenz} and {Paulo César RibeiroBoasquevisque} and {Robson DettmannJarske} and {Célio Siman MafraNunes} and {Isabela Passos PereiraQuintaes,} and {Samuel SantanaSodré} and {Universidade Vila Velha, Espirito Santo, Brazil}},
	journal={{Global Journal of Medical Research}},
	journal={{GJMR}}2249-46180975-588810.34257/GJMR,
	address={Cambridge, United States},
	publisher={Global Journals Organisation}2333945
}

@incollection{b0,
	
	
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	title={{Breast Cancer Facts & Figures}}
	
	
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	journal={{Atlanta}}
		
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	year={2019-2020. 2019}
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}

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	title={{Classification of breast cancer histology images using Convolutional Neural Networks}}
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	title={{Systematic analysis of breast cancer morphology uncovers stromal features associated with survival}}
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}

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}

@incollection{b6,
	
	
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@incollection{b8,
	
	
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@incollection{b9,
	
	
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@incollection{b11,
	
	
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	title={{The future of telepathology for the developing world}}
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	title={{Incidência do Câncer no Brasil}}
		
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@book{b13,
	
	
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	title={{Scoring diverse cellular morphologies in image-based screens with iterative feedback and machine learning}}
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@incollection{b16,
	
	
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	title={{Nuclear shape and orientation features from H&E images predict survival in early-stage estrogen receptor-positive breast cancers}}
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@incollection{b19,
	
	
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	title={{Automated image analysis in histopathology: a valuable tool in medical diagnostics. Expert VerMolDiagn}}
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@incollection{b22,
	
	
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@incollection{b25,
	
	
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@incollection{b26,
	
	
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@incollection{b27,
	
	
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	title={{Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images}}
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@incollection{b28,
	
	
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}