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Gastric cancer
Deep learning in gastric tissue diseases: a systematic review

Authors

  • Wanderson Gonçalves e Gonçalves Laboratório de Genética Humana e Médica - Instituto de Ciências Biológicas, Universidade Federal do Pará, Belém, Pará, BrazilNúcleo de Pesquisas em Oncologia, Universidade Federal do Pará, Belém, Pará, Brazil PubMed articlesGoogle scholar articles
  • Marcelo Henrique de Paula dos Santos Engenharia da Computação, Universidade Federal do Pará, Belém, Pará, Brazil PubMed articlesGoogle scholar articles
  • Fábio Manoel França Lobato Instituto de Engenharia e Geociências, Universidade Federal do Oeste do Pará, Santarém, Pará, Brazil PubMed articlesGoogle scholar articles
  • Ândrea Ribeiro-dos-Santos Laboratório de Genética Humana e Médica - Instituto de Ciências Biológicas, Universidade Federal do Pará, Belém, Pará, BrazilNúcleo de Pesquisas em Oncologia, Universidade Federal do Pará, Belém, Pará, Brazil PubMed articlesGoogle scholar articles
  • Gilderlanio Santana de Araújo Laboratório de Genética Humana e Médica - Instituto de Ciências Biológicas, Universidade Federal do Pará, Belém, Pará, Brazil PubMed articlesGoogle scholar articles
  1. Correspondence to Dr Gilderlanio Santana de Araújo; gilderlanio{at}gmail.com
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Citation

Gonçalves WGE, Santos MHDPD, Lobato FMF, et al
Deep learning in gastric tissue diseases: a systematic review
BMJ Open Gastroenterology 2020;7:e000371. doi: 10.1136/bmjgast-2019-000371

Publication history

  • Received December 31, 2019
  • Revised February 14, 2020
  • Accepted February 24, 2020
  • First published March 26, 2020.
Online issue publication 
January 08, 2022

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© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. http://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.