| Texto completo | |
| Autor(es): Mostrar menos - |
Rocha, Bruno Aragao
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Ferreira, Lorena Carneiro
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Rocha Vianna, Luis Gustavo
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Gomes Ferreira, Luma Gallacio
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Martins Ciconelle, Ana Claudia
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Da Silva Noronha, Alex
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Martins Cortez Filho, Joao
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Lima Nogueira, Lucas Salume
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Rocha Sampaio Leite, Jean Michel
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da Silva Filho, Mauricio Ricardo Moreira
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da Costa Leite, Claudia
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de Maria Felix, Marcelo
;
Gutierrez, Marco Antonio
;
Nomura, Cesar Higa
;
Cerri, Giovanni Guido
;
Carrilho, Flair Jose
;
Ono, Suzane Kioko
Número total de Autores: 17
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| Tipo de documento: | Artigo Científico |
| Fonte: | SCIENTIFIC REPORTS; v. 12, n. 1, p. 12-pg., 2022-11-24. |
| Resumo | |
Hepatocellular carcinoma (HCC) has become the 4th leading cause of cancer-related deaths, with high social, economical and health implications. Imaging techniques such as multiphase computed tomography (CT) have been successfully used for diagnosis of liver tumors such as HCC in a feasible and accurate way and its interpretation relies mainly on comparing the appearance of the lesions in the different contrast phases of the exam. Recently, some researchers have been dedicated to the development of tools based on machine learning (ML) algorithms, especially by deep learning techniques, to improve the diagnosis of liver lesions in imaging exams. However, the lack of standardization in the naming of the CT contrast phases in the DICOM metadata is a problem for real-life deployment of machine learning tools. Therefore, it is important to correctly identify the exam phase based only on the image and not on the exam metadata, which is unreliable. Motivated by this problem, we successfully created an annotation platform and implemented a convolutional neural network (CNN) to automatically identify the CT scan phases in the HCFMUSP database in the city of SAo Paulo, Brazil. We improved this algorithm with hyperparameter tuning and evaluated it with cross validation methods. Comparing its predictions with the radiologists annotation, it achieved an accuracy of 94.6%, 98% and 100% in the testing dataset for the slice, volume and exam evaluation, respectively. (AU) | |
| Processo FAPESP: | 20/01079-3 - LivIA: ferramenta de auxílio ao diagnóstico de lesões hepáticas |
| Beneficiário: | Jean Michel Rocha Sampaio Leite |
| Modalidade de apoio: | Bolsas no Brasil - Programa Capacitação - Treinamento Técnico |
| Processo FAPESP: | 20/00037-5 - LivIA: ferramenta de auxílio ao diagnóstico de lesões hepáticas |
| Beneficiário: | Luis Gustavo Rocha Vianna |
| Modalidade de apoio: | Bolsas no Brasil - Pesquisa Inovativa em Pequenas Empresas - PIPE |
| Processo FAPESP: | 21/04199-2 - LivIA: ferramenta de auxílio ao diagnóstico de lesões hepáticas |
| Beneficiário: | Luma Gallacio Gomes Ferreira |
| Modalidade de apoio: | Bolsas no Brasil - Programa Capacitação - Treinamento Técnico |
| Processo FAPESP: | 19/05723-7 - LivIA - ferramenta de auxílio ao diagnóstico de lesões hepáticas |
| Beneficiário: | Luis Gustavo Rocha Vianna |
| Modalidade de apoio: | Auxílio à Pesquisa - Pesquisa Inovativa em Pequenas Empresas - PIPE |
| Processo FAPESP: | 20/07411-0 - LivIA: ferramenta de auxílio ao diagnóstico de lesões hepáticas |
| Beneficiário: | João Martins Cortez Filho |
| Modalidade de apoio: | Bolsas no Brasil - Programa Capacitação - Treinamento Técnico |