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DIGITALIZAÇÃO DA VETERINÁRIA NA ERA DA INTELIGÊNCIA ARTIFICIAL: REVISÃO ABRANGENTES DOS AVANÇOS TECNOLÓGICOS E PERSPECTIVAS DIAGNÓSTICAS

Authors/Creators

  • 1. FACUMINAS-MG
  • 2. Universidade Federal da Paraíba-UFPB-PB
  • 3. Universidade Anhanguera Educacional - Pirituba SP
  • 4. Universidade Nilton Lins-Amazonas-AM
  • 5. Universidade Federal da Paraíba- UFPB-PB

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RESUMO SIMPLES

 

 

 

DIGITALIZAÇÃO DA VETERINÁRIA NA ERA DA INTELIGÊNCIA ARTIFICIAL: REVISÃO ABRANGENTES DOS AVANÇOS TECNOLÓGICOS E PERSPECTIVAS DIAGNÓSTICAS

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RESUMO_SIMPLES_DIGITALIZAÇÃO DA VETERINÁRIA NA ERA DA INTELIGÊNCIA ARTIFICIAL_DOI.pdf

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ISBN
978-65-982433-6-4

Software

Repository URL
https://www.even3.com.br/e/ii-congresso-periodico-nacional-em-medicina-veterinaria-consvet-633408

References

  • BERA, Kaustav; SCHALPER, Kurt A.; RIMM, David L. Artificial intelligence and machine learning in digital pathology: current state of the art and future directions. The Journal of Pathology, v. 251, n. 2, p. 149-160, 2020. CAMPANELLA, Gabriele et al. Clinical-grade computational pathology using convolutional neural networks. Nature Medicine, v. 25, n. 8, p. 1301-1309, 2019. FARAHANI, Keyvan; SORICH, Michael; WARRIER, Shripad. Deep learning based radiomics and radiogenomics for cancer detection. Nature Reviews Cancer, v. 21, n. 12, p. 738-752, 2021. GURCAN, Metin N. et al. Histopathological image analysis: a review. IEEE Reviews in Biomedical Engineering, v. 2, p. 147-171, 2009. JANOWCZYK, Andrew; MADABHUSHI, Anant. Deep learning for digital pathology image analysis: a comprehensive tutorial with selected use cases. Journal of Pathology Informatics, v. 7, p. 29, 2016. KOMURA, Daichi; ISHIKAWA, Shumpei. Machine learning methods for histopathological image analysis: a review. The Journal of Pathology, v. 244, n. 5, p. 608-618, 2018. LECUN, Yann; BENGIO, Yoshua; HINTON, Geoffrey E. Deep learning. Nature, v. 521, n. 7553, p. 436-444, 2015. LITJENS, Geert et al. A survey on deep learning in medical image analysis. IEEE Transactions on Medical Imaging, v. 38, n. 2, p. 760-774, 2019. MADABHUSHI, Anant; LEE, George. Image analysis and multimodal microscopy for detection of cancer. Nature Reviews Cancer, v. 16, n. 12, p. 735-745, 2016. RAJKOMAR, Alvin; DEAN, Jeffrey; KOHANE, Isaac S. Machine learning in medicine. The New England Journal of Medicine, v. 380, n. 14, p. 1347-1358, 2019. RONNEBERGER, Olaf; FISCHER, Philipp; BROX, Thomas. U-Net: convolutional networks for biomedical image segmentation. In: MICCAI 2015: MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION. Proceedings. Cham: Springer, 2015. p. 234-241. SIMONYAN, Karen; ZISSERMAN, Andrew. Very deep convolutional networks for large-scale image recognition. In: INTERNATIONAL CONFERENCE ON LEARNING REPRESENTATIONS. Proceedings. San Diego: ICLR, 2015. p. 1-14. SZEGEDY, Christian et al. Going deeper with convolutions. In: IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION. Proceedings. Boston: IEEE, 2015. p. 1-9. VETA, Mitko; PLUIM, Josien P. W.; VIERGEVER, Max A. Detecting cancer metastases on gigapixel pathology images. IEEE Transactions on Medical Imaging, v. 36, n. 2, p. 386-396, 2017.