Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images (2018)
- Authors:
- Saltz, Joel
- Gupta, Rajarsi
- Hou, Le
- Kurc, Tahsin
- Singh, Pankaj
- Nguyen, Vu
- Samaras, Dimitris
- Shroyer, Kenneth R.
- Zhao, Tianhao
- Batiste, Rebecca
- Van Arnam, John
- Shmulevich, Ilya
- Rao, Arvind
- Lazar, Alexander J.
- Sharma, Ashish
- Thorsson, Vésteinn
- Noushmehr, Houtan
- Carlotti Júnior, Carlos Gilberto
- Santos, José Sebastião dos
- Kemp, Rafael
- Sankarankuty, Ajith Kumar
- Tirapelli, Daniela Pretti da Cunha
- USP affiliated authors: NOUSHMEHR, HOUTAN - FMRP ; CARLOTTI JUNIOR, CARLOS GILBERTO - FMRP ; SANTOS, JOSÉ SEBASTIÃO DOS - FMRP ; KEMP, RAFAEL - FMRP ; SANKARANKUTTY, AJITH KUMAR - FMRP ; TIRAPELLI, DANIELA PRETTI DA CUNHA - FMRP
- Unidade: FMRP
- DOI: 10.1016/j.celrep.2018.03.086
- Subjects: NEOPLASIAS; APRENDIZADO COMPUTACIONAL; INTELIGÊNCIA ARTIFICIAL; BIOINFORMÁTICA; LINFÓCITOS
- Keywords: Digital pathology; Immuno-oncology; Machine learning; Lymphocytes; Tumor microenvironment; Deep learning; Tumor-infiltrating lymphocytes; Artificial intelligence; Bioinformatics; Computer vision
- Language: Inglês
- Imprenta:
- Source:
- Título: Cell Reports
- ISSN: 2211-1247
- Volume/Número/Paginação/Ano: v. 23, n. 1, p. 181-193, 2018
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
SALTZ, Joel et al. Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images. Cell Reports, v. 23, n. 1, p. 181-193, 2018Tradução . . Disponível em: https://doi.org/10.1016/j.celrep.2018.03.086. Acesso em: 14 fev. 2026. -
APA
Saltz, J., Gupta, R., Hou, L., Kurc, T., Singh, P., Nguyen, V., et al. (2018). Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images. Cell Reports, 23( 1), 181-193. doi:10.1016/j.celrep.2018.03.086 -
NLM
Saltz J, Gupta R, Hou L, Kurc T, Singh P, Nguyen V, Samaras D, Shroyer KR, Zhao T, Batiste R, Van Arnam J, Shmulevich I, Rao A, Lazar AJ, Sharma A, Thorsson V, Noushmehr H, Carlotti Júnior CG, Santos JS dos, Kemp R, Sankarankuty AK, Tirapelli DP da C. Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images [Internet]. Cell Reports. 2018 ; 23( 1): 181-193.[citado 2026 fev. 14 ] Available from: https://doi.org/10.1016/j.celrep.2018.03.086 -
Vancouver
Saltz J, Gupta R, Hou L, Kurc T, Singh P, Nguyen V, Samaras D, Shroyer KR, Zhao T, Batiste R, Van Arnam J, Shmulevich I, Rao A, Lazar AJ, Sharma A, Thorsson V, Noushmehr H, Carlotti Júnior CG, Santos JS dos, Kemp R, Sankarankuty AK, Tirapelli DP da C. Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images [Internet]. Cell Reports. 2018 ; 23( 1): 181-193.[citado 2026 fev. 14 ] Available from: https://doi.org/10.1016/j.celrep.2018.03.086 - Integrated genomic characterization of oesophageal carcinoma
- Comprehensive analysis of alternative splicing across tumors from 8,705 patients
- Scalable open science approach for mutation calling of tumor exomes using multiple genomic pipelines
- Molecular characterization and clinical relevance of metabolic expression subtypes in human cancers
- Pan-cancer analysis of lncRNA regulation supports their targeting of cancer genes in each tumor context
- Comprehensive molecular characterization of the hippo signaling pathway in cancer
- Pan-cancer alterations of the MYC oncogene and its proximal Network across the Cancer Genome Atlas
- Perspective on oncogenic processes at the end of the beginning of cancer genomics
- Oncogenic signaling pathways in the cancer genome atlas
- Systematic analysis of splice-site-creating mutations in cancer
Informações sobre o DOI: 10.1016/j.celrep.2018.03.086 (Fonte: oaDOI API)
Download do texto completo
| Tipo | Nome | Link | |
|---|---|---|---|
 |
002938421.pdf | Direct link |
How to cite
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
