Clinicians' perception of oral potentially malignant disorders: a pitfall for image annotation in supervised learning (2023)
- Authors:
- Araujo, Anna Luiza Damaceno
- Souza, Eduardo Santos Carlos de
- Faustino, Isabel Schausltz Pereira
- Saldivia-Siracusa, Cristina
- Brito-Sarracino, Tamires
- Lopes, Márcio Ajudarte
- Vargas, Pablo Agustin
- Pearson, Alexander T
- Kowalski, Luiz Paulo
- Carvalho, André Carlos Ponce de Leon Ferreira de
- Santos-Silva, Alan Roger dos
- Araujo, Anna Luiza Damaceno
- USP affiliated authors: KOWALSKI, LUIZ PAULO - FM ; CARVALHO, ANDRÉ CARLOS PONCE DE LEON FERREIRA DE - ICMC ; ARAÚJO, ANNA LUÍZA DAMACENO - FM ; SOUZA, EDUARDO SANTOS CARLOS DE - ICMC ; SILVA, TAMIRES BRITO DA - ICMC
- Unidades: FM; ICMC
- DOI: 10.1016/j.oooo.2023.02.018
- Subjects: APRENDIZADO COMPUTACIONAL; RECONHECIMENTO DE IMAGEM; NEOPLASIAS BUCAIS; NEOPLASIAS DE CABEÇA E PESCOÇO; LEUCOPLASIA BUCAL; INTELIGÊNCIA ARTIFICIAL
- Keywords: Artificial Intelligence; Supervised Learning; Annotation; Oral Potentially Malignant Disorder
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology
- ISSN: 2212-4403
- Volume/Número/Paginação/Ano: v. 136, n. 3, p. 315-321, Sept. 2023
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
ARAUJO, Anna Luiza Damaceno et al. Clinicians' perception of oral potentially malignant disorders: a pitfall for image annotation in supervised learning. Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology, v. 136, n. 3, p. 315-321, 2023Tradução . . Disponível em: https://doi.org/10.1016/j.oooo.2023.02.018. Acesso em: 03 jan. 2026. -
APA
Araujo, A. L. D., Souza, E. S. C. de, Faustino, I. S. P., Saldivia-Siracusa, C., Brito-Sarracino, T., Lopes, M. A., et al. (2023). Clinicians' perception of oral potentially malignant disorders: a pitfall for image annotation in supervised learning. Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology, 136( 3), 315-321. doi:10.1016/j.oooo.2023.02.018 -
NLM
Araujo ALD, Souza ESC de, Faustino ISP, Saldivia-Siracusa C, Brito-Sarracino T, Lopes MA, Vargas PA, Pearson AT, Kowalski LP, Carvalho ACP de LF de, Santos-Silva AR dos. Clinicians' perception of oral potentially malignant disorders: a pitfall for image annotation in supervised learning [Internet]. Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology. 2023 ; 136( 3): 315-321.[citado 2026 jan. 03 ] Available from: https://doi.org/10.1016/j.oooo.2023.02.018 -
Vancouver
Araujo ALD, Souza ESC de, Faustino ISP, Saldivia-Siracusa C, Brito-Sarracino T, Lopes MA, Vargas PA, Pearson AT, Kowalski LP, Carvalho ACP de LF de, Santos-Silva AR dos. Clinicians' perception of oral potentially malignant disorders: a pitfall for image annotation in supervised learning [Internet]. Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology. 2023 ; 136( 3): 315-321.[citado 2026 jan. 03 ] Available from: https://doi.org/10.1016/j.oooo.2023.02.018 - A convolutional neural network framework with ConvNeXt and Grad-CAM for classification of oral potentially malignant disorders and oral squamous cell carcinoma
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- Machine learning concepts applied to oral pathology and oral medicine: a convolutional neural networks' approach
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- Deep learning for automatic segmentation of clinical photographs of oral premalignant and malignant lesions
- Segmentation of oral lesions through convolutional neural networks
Informações sobre o DOI: 10.1016/j.oooo.2023.02.018 (Fonte: oaDOI API)
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