Deep neural network uncertainty estimation for early oral cancer diagnosis.
J Oral Pathol Med
; 53(5): 294-302, 2024 May.
Article
em En
| MEDLINE
| ID: mdl-38632703
ABSTRACT
BACKGROUND:
Early diagnosis in oral cancer is essential to reduce both morbidity and mortality. This study explores the use of uncertainty estimation in deep learning for early oral cancer diagnosis.METHODS:
We develop a Bayesian deep learning model termed 'Probabilistic HRNet', which utilizes the ensemble MC dropout method on HRNet. Additionally, two oral lesion datasets with distinct distributions are created. We conduct a retrospective study to assess the predictive performance and uncertainty of Probabilistic HRNet across these datasets.RESULTS:
Probabilistic HRNet performs optimally on the In-domain test set, achieving an F1 score of 95.3% and an AUC of 96.9% by excluding the top 30% high-uncertainty samples. For evaluations on the Domain-shift test set, the results show an F1 score of 64.9% and an AUC of 80.3%. After excluding 30% of the high-uncertainty samples, these metrics improve to an F1 score of 74.4% and an AUC of 85.6%.CONCLUSION:
Redirecting samples with high uncertainty to experts for subsequent diagnosis significantly decreases the rates of misdiagnosis, which highlights that uncertainty estimation is vital to ensure safe decision making for computer-aided early oral cancer diagnosis.Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Neoplasias Bucais
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Teorema de Bayes
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Detecção Precoce de Câncer
/
Aprendizado Profundo
Limite:
Humans
Idioma:
En
Revista:
J Oral Pathol Med
/
J. oral pathol. med
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Journal of oral pathology and medicine
Assunto da revista:
ODONTOLOGIA
/
PATOLOGIA
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
China