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Trabalho de evento-resumo periodico
Deep extracted features to support content-based image retrieval systems in the diagnosis of Covid-19 and interstitial diseases (2022)
Bêdo, Marcos Vinícius Naves; Lima, L; Koenigkam-Santos, Marcel ; Traina, Agma Juci Machado ; Azevedo-Marques, Paulo Mazzoncini de
Source: International Journal of Computer Assisted Radiology and Surgery. Conference titles: International Congress and Exhibition on Computer Assisted Radiology and Surgery - CARS. Unidades: FMRP, ICMC
Subjects: RECUPERAÇÃO DA INFORMAÇÃO, RECONHECIMENTO DE IMAGEM, APRENDIZADO COMPUTACIONAL, REDES NEURAIS, COVID-19
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ABNT
BÊDO, Marcos Vinícius Naves et al. Deep extracted features to support content-based image retrieval systems in the diagnosis of Covid-19 and interstitial diseases. International Journal of Computer Assisted Radiology and Surgery. Heidelberg: Springer. Disponível em: https://doi.org/10.1007/s11548-022-02635-x. Acesso em: 16 maio 2024. , 2022
APA
Bêdo, M. V. N., Lima, L., Koenigkam-Santos, M., Traina, A. J. M., & Azevedo-Marques, P. M. de. (2022). Deep extracted features to support content-based image retrieval systems in the diagnosis of Covid-19 and interstitial diseases. International Journal of Computer Assisted Radiology and Surgery. Heidelberg: Springer. doi:10.1007/s11548-022-02635-x
NLM
Bêdo MVN, Lima L, Koenigkam-Santos M, Traina AJM, Azevedo-Marques PM de. Deep extracted features to support content-based image retrieval systems in the diagnosis of Covid-19 and interstitial diseases [Internet]. International Journal of Computer Assisted Radiology and Surgery. 2022 ; 17 S13-S14.[citado 2024 maio 16 ] Available from: https://doi.org/10.1007/s11548-022-02635-x
Vancouver
Bêdo MVN, Lima L, Koenigkam-Santos M, Traina AJM, Azevedo-Marques PM de. Deep extracted features to support content-based image retrieval systems in the diagnosis of Covid-19 and interstitial diseases [Internet]. International Journal of Computer Assisted Radiology and Surgery. 2022 ; 17 S13-S14.[citado 2024 maio 16 ] Available from: https://doi.org/10.1007/s11548-022-02635-x
Trabalho de evento-resumo periodico
Spine MRI texture analysis and prediction of osteoporotic vertebral fracture (2021)
Ramos, Jonathan da Silva ; Maciel, Jamilly Gomes; Traina, Agma Juci Machado ; Azevedo-Marques, Paulo Mazzoncini de ; Nogueira-Barbosa, Marcello Henrique
Source: International Journal of Computer Assisted Radiology and Surgery. Conference titles: International Congress and Exhibition on Computer Assisted Radiology and Surgery - CARS. Unidades: ICMC, FMRP
Subjects: BANCO DE DADOS MULTIMÍDIA, RECONHECIMENTO DE IMAGEM, DIAGNÓSTICO POR IMAGEM, OSTEOPOROSE, COLUNA VERTEBRAL, RESSONÂNCIA MAGNÉTICA, TECNOLOGIAS DA SAÚDE
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ABNT
RAMOS, Jonathan da Silva et al. Spine MRI texture analysis and prediction of osteoporotic vertebral fracture. International Journal of Computer Assisted Radiology and Surgery. Heidelberg: Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo. Disponível em: https://doi.org/10.1007/s11548-021-02375-4. Acesso em: 16 maio 2024. , 2021
APA
Ramos, J. da S., Maciel, J. G., Traina, A. J. M., Azevedo-Marques, P. M. de, & Nogueira-Barbosa, M. H. (2021). Spine MRI texture analysis and prediction of osteoporotic vertebral fracture. International Journal of Computer Assisted Radiology and Surgery. Heidelberg: Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo. doi:10.1007/s11548-021-02375-4
NLM
Ramos J da S, Maciel JG, Traina AJM, Azevedo-Marques PM de, Nogueira-Barbosa MH. Spine MRI texture analysis and prediction of osteoporotic vertebral fracture [Internet]. International Journal of Computer Assisted Radiology and Surgery. 2021 ; 16 S115-S116.[citado 2024 maio 16 ] Available from: https://doi.org/10.1007/s11548-021-02375-4
Vancouver
Ramos J da S, Maciel JG, Traina AJM, Azevedo-Marques PM de, Nogueira-Barbosa MH. Spine MRI texture analysis and prediction of osteoporotic vertebral fracture [Internet]. International Journal of Computer Assisted Radiology and Surgery. 2021 ; 16 S115-S116.[citado 2024 maio 16 ] Available from: https://doi.org/10.1007/s11548-021-02375-4
Trabalho de evento-resumo periodico
AI-based radiomic approach in high-resolution CT images for differential diagnosis of idiopathic pulmonary fibrosis (2020)
Dorileo, Éderson Antonio Gomes; Koenigkam-Santos, Marcel ; Fabro, Alexandre Todorovic ; Azevedo-Marques, Paulo Mazzoncini de
Source: International Journal of Computer Assisted Radiology and Surgery. Conference titles: International Congress and Exhibition on Computer Assisted Radiology and Surgery - CARS. Unidade: FMRP
Subjects: FIBROSE PULMONAR, BIOMARCADORES, INTELIGÊNCIA ARTIFICIAL, RADIOLOGIA, DIAGNÓSTICO POR COMPUTADOR
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ABNT
DORILEO, Éderson Antonio Gomes et al. AI-based radiomic approach in high-resolution CT images for differential diagnosis of idiopathic pulmonary fibrosis. International Journal of Computer Assisted Radiology and Surgery. Heidelberg: Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo. Disponível em: https://doi.org/10.1007/s11548-020-02171-6. Acesso em: 16 maio 2024. , 2020
APA
Dorileo, É. A. G., Koenigkam-Santos, M., Fabro, A. T., & Azevedo-Marques, P. M. de. (2020). AI-based radiomic approach in high-resolution CT images for differential diagnosis of idiopathic pulmonary fibrosis. International Journal of Computer Assisted Radiology and Surgery. Heidelberg: Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo. doi:10.1007/s11548-020-02171-6
NLM
Dorileo ÉAG, Koenigkam-Santos M, Fabro AT, Azevedo-Marques PM de. AI-based radiomic approach in high-resolution CT images for differential diagnosis of idiopathic pulmonary fibrosis [Internet]. International Journal of Computer Assisted Radiology and Surgery. 2020 ; 15 S120-S121.[citado 2024 maio 16 ] Available from: https://doi.org/10.1007/s11548-020-02171-6
Vancouver
Dorileo ÉAG, Koenigkam-Santos M, Fabro AT, Azevedo-Marques PM de. AI-based radiomic approach in high-resolution CT images for differential diagnosis of idiopathic pulmonary fibrosis [Internet]. International Journal of Computer Assisted Radiology and Surgery. 2020 ; 15 S120-S121.[citado 2024 maio 16 ] Available from: https://doi.org/10.1007/s11548-020-02171-6
Trabalho de evento-resumo periodico
Towards convolutional neural network on primary lung tumors to predict histophatological type, distant and lymph node metastasis (2020)
Lima, Lucas Lins de; Ferreiro Junior, J. R.; Fabro, Alexandre Todorovic ; Cipriano, Federico; Faccio, A.; Koenigkam-Santos, Marcel ; Azevedo-Marques, Paulo Mazzoncini de
Source: International Journal of Computer Assisted Radiology and Surgery. Conference titles: International Congress and Exhibition on Computer Assisted Radiology and Surgery - CARS. Unidade: FMRP
Subjects: NEOPLASIAS PULMONARES, METÁSTASE ANIMAL, REDES NEURAIS, DIAGNÓSTICO POR COMPUTADOR, APRENDIZADO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
LIMA, Lucas Lins de et al. Towards convolutional neural network on primary lung tumors to predict histophatological type, distant and lymph node metastasis. International Journal of Computer Assisted Radiology and Surgery. Heidelberg: Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo. Disponível em: https://doi.org/10.1007/s11548-020-02171-6. Acesso em: 16 maio 2024. , 2020
APA
Lima, L. L. de, Ferreiro Junior, J. R., Fabro, A. T., Cipriano, F., Faccio, A., Koenigkam-Santos, M., & Azevedo-Marques, P. M. de. (2020). Towards convolutional neural network on primary lung tumors to predict histophatological type, distant and lymph node metastasis. International Journal of Computer Assisted Radiology and Surgery. Heidelberg: Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo. doi:10.1007/s11548-020-02171-6
NLM
Lima LL de, Ferreiro Junior JR, Fabro AT, Cipriano F, Faccio A, Koenigkam-Santos M, Azevedo-Marques PM de. Towards convolutional neural network on primary lung tumors to predict histophatological type, distant and lymph node metastasis [Internet]. International Journal of Computer Assisted Radiology and Surgery. 2020 ; 15 S116-S117.[citado 2024 maio 16 ] Available from: https://doi.org/10.1007/s11548-020-02171-6
Vancouver
Lima LL de, Ferreiro Junior JR, Fabro AT, Cipriano F, Faccio A, Koenigkam-Santos M, Azevedo-Marques PM de. Towards convolutional neural network on primary lung tumors to predict histophatological type, distant and lymph node metastasis [Internet]. International Journal of Computer Assisted Radiology and Surgery. 2020 ; 15 S116-S117.[citado 2024 maio 16 ] Available from: https://doi.org/10.1007/s11548-020-02171-6
Trabalho de evento-resumo periodico
Machine learning models to predict axial and peripheral spondyloarthritis: a comparative radiomic study between SPAIR and STIR MRI sequences (2020)
Tenório, Ariane Priscilla Magalhães; Faleiros, M. C.; Ferreira-Junior, J. R.; Dalto, V. F.; Assad, R. L.; Nogueira-Barbosa, Marcello Henrique ; Azevedo-Marques, Paulo Mazzoncini de
Source: International Journal of Computer Assisted Radiology and Surgery. Conference titles: International Congress and Exhibition on Computer Assisted Radiology and Surgery - CARS. Unidade: FMRP
Subjects: DIAGNÓSTICO POR COMPUTADOR, APRENDIZADO COMPUTACIONAL, RADIOLOGIA, REUMATOLOGIA, RESSONÂNCIA MAGNÉTICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
TENÓRIO, Ariane Priscilla Magalhães et al. Machine learning models to predict axial and peripheral spondyloarthritis: a comparative radiomic study between SPAIR and STIR MRI sequences. International Journal of Computer Assisted Radiology and Surgery. Heidelberg: Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo. Disponível em: https://doi.org/10.1007/s11548-020-02171-6. Acesso em: 16 maio 2024. , 2020
APA
Tenório, A. P. M., Faleiros, M. C., Ferreira-Junior, J. R., Dalto, V. F., Assad, R. L., Nogueira-Barbosa, M. H., & Azevedo-Marques, P. M. de. (2020). Machine learning models to predict axial and peripheral spondyloarthritis: a comparative radiomic study between SPAIR and STIR MRI sequences. International Journal of Computer Assisted Radiology and Surgery. Heidelberg: Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo. doi:10.1007/s11548-020-02171-6
NLM
Tenório APM, Faleiros MC, Ferreira-Junior JR, Dalto VF, Assad RL, Nogueira-Barbosa MH, Azevedo-Marques PM de. Machine learning models to predict axial and peripheral spondyloarthritis: a comparative radiomic study between SPAIR and STIR MRI sequences [Internet]. International Journal of Computer Assisted Radiology and Surgery. 2020 ; 15 S129-S130.[citado 2024 maio 16 ] Available from: https://doi.org/10.1007/s11548-020-02171-6
Vancouver
Tenório APM, Faleiros MC, Ferreira-Junior JR, Dalto VF, Assad RL, Nogueira-Barbosa MH, Azevedo-Marques PM de. Machine learning models to predict axial and peripheral spondyloarthritis: a comparative radiomic study between SPAIR and STIR MRI sequences [Internet]. International Journal of Computer Assisted Radiology and Surgery. 2020 ; 15 S129-S130.[citado 2024 maio 16 ] Available from: https://doi.org/10.1007/s11548-020-02171-6
Trabalho de evento-resumo periodico
Radiomic biomarkers for sacroiliitis diagnosis and prediction of spondyloarthritis progression (2020)
Tenório, Ariane Priscilla Magalhães; Faleiros, M. C.; Ferreira-Junior, J. R.; Dalto, V. F.; Assad, R. L.; Nogueira-Barbosa, Marcello Henrique ; Azevedo-Marques, Paulo Mazzoncini de
Source: International Journal of Computer Assisted Radiology and Surgery. Conference titles: International Congress and Exhibition on Computer Assisted Radiology and Surgery - CARS. Unidade: FMRP
Subjects: REUMATOLOGIA, RESSONÂNCIA MAGNÉTICA, DIAGNÓSTICO POR COMPUTADOR, APRENDIZADO COMPUTACIONAL, BIOMARCADORES
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
TENÓRIO, Ariane Priscilla Magalhães et al. Radiomic biomarkers for sacroiliitis diagnosis and prediction of spondyloarthritis progression. International Journal of Computer Assisted Radiology and Surgery. Heidelberg: Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo. Disponível em: https://doi.org/10.1007/s11548-020-02171-6. Acesso em: 16 maio 2024. , 2020
APA
Tenório, A. P. M., Faleiros, M. C., Ferreira-Junior, J. R., Dalto, V. F., Assad, R. L., Nogueira-Barbosa, M. H., & Azevedo-Marques, P. M. de. (2020). Radiomic biomarkers for sacroiliitis diagnosis and prediction of spondyloarthritis progression. International Journal of Computer Assisted Radiology and Surgery. Heidelberg: Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo. doi:10.1007/s11548-020-02171-6
NLM
Tenório APM, Faleiros MC, Ferreira-Junior JR, Dalto VF, Assad RL, Nogueira-Barbosa MH, Azevedo-Marques PM de. Radiomic biomarkers for sacroiliitis diagnosis and prediction of spondyloarthritis progression [Internet]. International Journal of Computer Assisted Radiology and Surgery. 2020 ; 15 S127-S129.[citado 2024 maio 16 ] Available from: https://doi.org/10.1007/s11548-020-02171-6
Vancouver
Tenório APM, Faleiros MC, Ferreira-Junior JR, Dalto VF, Assad RL, Nogueira-Barbosa MH, Azevedo-Marques PM de. Radiomic biomarkers for sacroiliitis diagnosis and prediction of spondyloarthritis progression [Internet]. International Journal of Computer Assisted Radiology and Surgery. 2020 ; 15 S127-S129.[citado 2024 maio 16 ] Available from: https://doi.org/10.1007/s11548-020-02171-6

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