Deep learning predicts underlying features on pathology images with therapeutic relevance for breast and gastric cancer (2020)
- Authors:
- Autor USP: MITROWSKY, RAFAEL ANDRES ROSALES - FFCLRP
- Unidade: FFCLRP
- DOI: 10.3390/cancers12123687
- Subjects: BIOMARCADORES; NEOPLASIAS; DNA; APRENDIZADO COMPUTACIONAL; TERAPÊUTICA MÉDICA; ALGORITMOS; CARCINOGÊNESE; IMUNOTERAPIA; RECOMBINAÇÃO GENÉTICA
- Keywords: Digital pathology; Deep learning; Mutational signature; Biomarker; DNA repair deficiency
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
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ABNT
VALIERIS, Renan et al. Deep learning predicts underlying features on pathology images with therapeutic relevance for breast and gastric cancer. Cancers, v. 12, n. 12, p. 1-12, 2020Tradução . . Disponível em: https://doi.org/10.3390/cancers12123687. Acesso em: 13 fev. 2026. -
APA
Valieris, R., Amaro, L., Osório, C. A. B. de T., Bueno, A. P., Mitrowsky, R. A. R., Carraro, D. M., et al. (2020). Deep learning predicts underlying features on pathology images with therapeutic relevance for breast and gastric cancer. Cancers, 12( 12), 1-12. doi:10.3390/cancers12123687 -
NLM
Valieris R, Amaro L, Osório CAB de T, Bueno AP, Mitrowsky RAR, Carraro DM, Nunes DN, Dias-Neto E, Silva IT da. Deep learning predicts underlying features on pathology images with therapeutic relevance for breast and gastric cancer [Internet]. Cancers. 2020 ; 12( 12): 1-12.[citado 2026 fev. 13 ] Available from: https://doi.org/10.3390/cancers12123687 -
Vancouver
Valieris R, Amaro L, Osório CAB de T, Bueno AP, Mitrowsky RAR, Carraro DM, Nunes DN, Dias-Neto E, Silva IT da. Deep learning predicts underlying features on pathology images with therapeutic relevance for breast and gastric cancer [Internet]. Cancers. 2020 ; 12( 12): 1-12.[citado 2026 fev. 13 ] Available from: https://doi.org/10.3390/cancers12123687 - HIV-1 integration landscape during latent and active infection
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Informações sobre o DOI: 10.3390/cancers12123687 (Fonte: oaDOI API)
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