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Trabalho de evento-resumo periodico
Application of Convolutional Neural Network with Transfer learning to pattern recognition of tuberculosis in chest X-ray images (2023)
Lima, Lucas Lins de; Rodrigues, R; Saito, J.; Yussuf, O.; Peron, L.; Lopes, K.; Koenigkam-Santos, Marcel ; Azevedo-Marques, Paulo Mazzoncini de
Fonte: International Journal of Computer Assisted Radiology and Surgery. Nome do evento: International Congress and Exhibition on Computer Assisted Radiology and Surgery - CARS. Unidades: FMBRU, FMRP, Interunidades em Bioengenharia
Assuntos: TUBERCULOSE, IMAGEM DIGITAL, RAIOS X, REDES NEURAIS
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ABNT
LIMA, Lucas Lins de et al. Application of Convolutional Neural Network with Transfer learning to pattern recognition of tuberculosis in chest X-ray images. International Journal of Computer Assisted Radiology and Surgery. Heidelberg: , Universidade de São Paulo. Disponível em: https://doi.org/10.1007/s11548-023-02878-2. Acesso em: 23 nov. 2025. , 2023
APA
Lima, L. L. de, Rodrigues, R., Saito, J., Yussuf, O., Peron, L., Lopes, K., et al. (2023). Application of Convolutional Neural Network with Transfer learning to pattern recognition of tuberculosis in chest X-ray images. International Journal of Computer Assisted Radiology and Surgery. Heidelberg: , Universidade de São Paulo. doi:10.1007/s11548-023-02878-2
NLM
Lima LL de, Rodrigues R, Saito J, Yussuf O, Peron L, Lopes K, Koenigkam-Santos M, Azevedo-Marques PM de. Application of Convolutional Neural Network with Transfer learning to pattern recognition of tuberculosis in chest X-ray images [Internet]. International Journal of Computer Assisted Radiology and Surgery. 2023 ; 18 68-70.[citado 2025 nov. 23 ] Available from: https://doi.org/10.1007/s11548-023-02878-2
Vancouver
Lima LL de, Rodrigues R, Saito J, Yussuf O, Peron L, Lopes K, Koenigkam-Santos M, Azevedo-Marques PM de. Application of Convolutional Neural Network with Transfer learning to pattern recognition of tuberculosis in chest X-ray images [Internet]. International Journal of Computer Assisted Radiology and Surgery. 2023 ; 18 68-70.[citado 2025 nov. 23 ] Available from: https://doi.org/10.1007/s11548-023-02878-2
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
Fonte: International Journal of Computer Assisted Radiology and Surgery. Nome do evento: International Congress and Exhibition on Computer Assisted Radiology and Surgery - CARS. Unidades: FMRP, ICMC
Assuntos: 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: 23 nov. 2025. , 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 2025 nov. 23 ] 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 2025 nov. 23 ] 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
Fonte: International Journal of Computer Assisted Radiology and Surgery. Nome do evento: International Congress and Exhibition on Computer Assisted Radiology and Surgery - CARS. Unidades: ICMC, FMRP
Assuntos: 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: 23 nov. 2025. , 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 2025 nov. 23 ] 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 2025 nov. 23 ] 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
Fonte: International Journal of Computer Assisted Radiology and Surgery. Nome do evento: International Congress and Exhibition on Computer Assisted Radiology and Surgery - CARS. Unidade: FMRP
Assuntos: 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: 23 nov. 2025. , 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 2025 nov. 23 ] 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 2025 nov. 23 ] 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
Fonte: International Journal of Computer Assisted Radiology and Surgery. Nome do evento: International Congress and Exhibition on Computer Assisted Radiology and Surgery - CARS. Unidade: FMRP
Assuntos: NEOPLASIAS PULMONARES, METÁSTASE ANIMAL, REDES NEURAIS, DIAGNÓSTICO POR COMPUTADOR, APRENDIZADO COMPUTACIONAL
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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: 23 nov. 2025. , 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 2025 nov. 23 ] 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 2025 nov. 23 ] 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
Fonte: International Journal of Computer Assisted Radiology and Surgery. Nome do evento: International Congress and Exhibition on Computer Assisted Radiology and Surgery - CARS. Unidade: FMRP
Assuntos: DIAGNÓSTICO POR COMPUTADOR, APRENDIZADO COMPUTACIONAL, RADIOLOGIA, REUMATOLOGIA, RESSONÂNCIA MAGNÉTICA
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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: 23 nov. 2025. , 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 2025 nov. 23 ] 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 2025 nov. 23 ] Available from: https://doi.org/10.1007/s11548-020-02171-6
Artigo de periodico
CT-based radiomics for prediction of histologic subtype and metastatic disease in primary malignant lung neoplasms (2020)
Ferreira Junior, José Raniery; Santos, Marcel Koenigkam ; Tenório, Ariane Priscilla Magalhães; Faleiros, Matheus Calil; Cipriano, Federico Enrique Garcia; Fabro, Alexandre Todorovic ; Näppi, Janne; Yoshida, Hiroyuki; Azevedo-Marques, Paulo Mazzoncini de
Fonte: International Journal of Computer Assisted Radiology and Surgery. Unidade: FMRP
Assuntos: RADIOMETRIA, NEOPLASIAS PULMONARES, BIOMARCADORES
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ABNT
FERREIRA JUNIOR, José Raniery et al. CT-based radiomics for prediction of histologic subtype and metastatic disease in primary malignant lung neoplasms. International Journal of Computer Assisted Radiology and Surgery, v. 15, p. 163-172, 2020Tradução . . Disponível em: https://doi.org/10.1007/s11548-019-02093-y. Acesso em: 23 nov. 2025.
APA
Ferreira Junior, J. R., Santos, M. K., Tenório, A. P. M., Faleiros, M. C., Cipriano, F. E. G., Fabro, A. T., et al. (2020). CT-based radiomics for prediction of histologic subtype and metastatic disease in primary malignant lung neoplasms. International Journal of Computer Assisted Radiology and Surgery, 15, 163-172. doi:10.1007/s11548-019-02093-y
NLM
Ferreira Junior JR, Santos MK, Tenório APM, Faleiros MC, Cipriano FEG, Fabro AT, Näppi J, Yoshida H, Azevedo-Marques PM de. CT-based radiomics for prediction of histologic subtype and metastatic disease in primary malignant lung neoplasms [Internet]. International Journal of Computer Assisted Radiology and Surgery. 2020 ; 15 163-172.[citado 2025 nov. 23 ] Available from: https://doi.org/10.1007/s11548-019-02093-y
Vancouver
Ferreira Junior JR, Santos MK, Tenório APM, Faleiros MC, Cipriano FEG, Fabro AT, Näppi J, Yoshida H, Azevedo-Marques PM de. CT-based radiomics for prediction of histologic subtype and metastatic disease in primary malignant lung neoplasms [Internet]. International Journal of Computer Assisted Radiology and Surgery. 2020 ; 15 163-172.[citado 2025 nov. 23 ] Available from: https://doi.org/10.1007/s11548-019-02093-y
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
Fonte: International Journal of Computer Assisted Radiology and Surgery. Nome do evento: International Congress and Exhibition on Computer Assisted Radiology and Surgery - CARS. Unidade: FMRP
Assuntos: REUMATOLOGIA, RESSONÂNCIA MAGNÉTICA, DIAGNÓSTICO POR COMPUTADOR, APRENDIZADO COMPUTACIONAL, BIOMARCADORES
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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: 23 nov. 2025. , 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 2025 nov. 23 ] 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 2025 nov. 23 ] Available from: https://doi.org/10.1007/s11548-020-02171-6
Artigo de periodico
A study of MRI-based radiomics biomarkers for sacroiliitis and spondyloarthritis (2020)
Tenório, Ariane Priscilla Magalhães; Faleiros, Matheus Calil; Ferreira Junior, José Raniery ; Dalto, Vitor Faeda ; Assad, Rodrigo Luppino ; Louzada Júnior, Paulo ; Yoshida, Hiroyuki; Nogueira-Barbosa, Marcello Henrique ; Azevedo-Marques, Paulo Mazzoncini de
Fonte: International Journal of Computer Assisted Radiology and Surgery. Unidade: FMRP
Assuntos: IMAGEM DIGITAL, RESSONÂNCIA MAGNÉTICA, BIOMARCADORES
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ABNT
TENÓRIO, Ariane Priscilla Magalhães et al. A study of MRI-based radiomics biomarkers for sacroiliitis and spondyloarthritis. International Journal of Computer Assisted Radiology and Surgery, v. 15, p. 1737-1748, 2020Tradução . . Disponível em: https://doi.org/10.1007/s11548-020-02219-7. Acesso em: 23 nov. 2025.
APA
Tenório, A. P. M., Faleiros, M. C., Ferreira Junior, J. R., Dalto, V. F., Assad, R. L., Louzada Júnior, P., et al. (2020). A study of MRI-based radiomics biomarkers for sacroiliitis and spondyloarthritis. International Journal of Computer Assisted Radiology and Surgery, 15, 1737-1748. doi:10.1007/s11548-020-02219-7
NLM
Tenório APM, Faleiros MC, Ferreira Junior JR, Dalto VF, Assad RL, Louzada Júnior P, Yoshida H, Nogueira-Barbosa MH, Azevedo-Marques PM de. A study of MRI-based radiomics biomarkers for sacroiliitis and spondyloarthritis [Internet]. International Journal of Computer Assisted Radiology and Surgery. 2020 ; 15 1737-1748.[citado 2025 nov. 23 ] Available from: https://doi.org/10.1007/s11548-020-02219-7
Vancouver
Tenório APM, Faleiros MC, Ferreira Junior JR, Dalto VF, Assad RL, Louzada Júnior P, Yoshida H, Nogueira-Barbosa MH, Azevedo-Marques PM de. A study of MRI-based radiomics biomarkers for sacroiliitis and spondyloarthritis [Internet]. International Journal of Computer Assisted Radiology and Surgery. 2020 ; 15 1737-1748.[citado 2025 nov. 23 ] Available from: https://doi.org/10.1007/s11548-020-02219-7
Trabalho de evento-resumo periodico
Machine learning based lung cancer diagnosis using post-contrast T1-weighted and T2-weighted magnetic resonance imaging (2019)
Francisco, Victor; Wada, Danilo Tadao; Ferreira Junior, José Raniery; Fabro, Alexandre Todorovic ; Cipriano, Federico Enrique Garcia; Ferreira, Luciana Pimentel; Quatrina, Sathya Geraldo; Santos, Marcel Koenigkam ; Azevedo-Marques, Paulo Mazzoncini de
Fonte: International Journal of Computer Assisted Radiology and Surgery. Nome do evento: Computer Assisted Radiology and Surgery (CARS) - International Congress and Exhibition. Unidades: FMRP, Interunidades em Bioengenharia
Assuntos: APRENDIZADO COMPUTACIONAL, NEOPLASIAS PULMONARES, RESSONÂNCIA MAGNÉTICA, DIAGNÓSTICO POR COMPUTADOR
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ABNT
FRANCISCO, Victor et al. Machine learning based lung cancer diagnosis using post-contrast T1-weighted and T2-weighted magnetic resonance imaging. 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/s11548-019-01969-3. Acesso em: 23 nov. 2025. , 2019
APA
Francisco, V., Wada, D. T., Ferreira Junior, J. R., Fabro, A. T., Cipriano, F. E. G., Ferreira, L. P., et al. (2019). Machine learning based lung cancer diagnosis using post-contrast T1-weighted and T2-weighted magnetic resonance imaging. International Journal of Computer Assisted Radiology and Surgery. Heidelberg: Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo. doi:10.1007/s11548-019-01969-3
NLM
Francisco V, Wada DT, Ferreira Junior JR, Fabro AT, Cipriano FEG, Ferreira LP, Quatrina SG, Santos MK, Azevedo-Marques PM de. Machine learning based lung cancer diagnosis using post-contrast T1-weighted and T2-weighted magnetic resonance imaging [Internet]. International Journal of Computer Assisted Radiology and Surgery. 2019 ; 14 S79-S80.[citado 2025 nov. 23 ] Available from: https://doi.org/10.1007/s11548-019-01969-3
Vancouver
Francisco V, Wada DT, Ferreira Junior JR, Fabro AT, Cipriano FEG, Ferreira LP, Quatrina SG, Santos MK, Azevedo-Marques PM de. Machine learning based lung cancer diagnosis using post-contrast T1-weighted and T2-weighted magnetic resonance imaging [Internet]. International Journal of Computer Assisted Radiology and Surgery. 2019 ; 14 S79-S80.[citado 2025 nov. 23 ] Available from: https://doi.org/10.1007/s11548-019-01969-3
Trabalho de evento-resumo periodico
A radiomics approach for differentiation of pseudocavitation from cavitation on lung cancer tumors (2019)
Ferreira Junior, José Raniery; Santos, Marcel Koenigkam ; Kikuchi, Y.; Faleiros, Matheus Calil; Cipriano, Federico Enrique Garcia; Fabro, Alexandre Todorovic ; Yoshida, H.; Azevedo-Marques, Paulo Mazzoncini de
Fonte: International Journal of Computer Assisted Radiology and Surgery. Nome do evento: Computer Assisted Radiology and Surgery (CARS) - International Congress and Exhibition. Unidade: FMRP
Assuntos: RADIOMETRIA, NEOPLASIAS PULMONARES, CAVITAÇÃO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
FERREIRA JUNIOR, José Raniery et al. A radiomics approach for differentiation of pseudocavitation from cavitation on lung cancer tumors. 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/s11548-019-01969-3. Acesso em: 23 nov. 2025. , 2019
APA
Ferreira Junior, J. R., Santos, M. K., Kikuchi, Y., Faleiros, M. C., Cipriano, F. E. G., Fabro, A. T., et al. (2019). A radiomics approach for differentiation of pseudocavitation from cavitation on lung cancer tumors. International Journal of Computer Assisted Radiology and Surgery. Heidelberg: Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo. doi:10.1007/s11548-019-01969-3
NLM
Ferreira Junior JR, Santos MK, Kikuchi Y, Faleiros MC, Cipriano FEG, Fabro AT, Yoshida H, Azevedo-Marques PM de. A radiomics approach for differentiation of pseudocavitation from cavitation on lung cancer tumors [Internet]. International Journal of Computer Assisted Radiology and Surgery. 2019 ; 14 S78.[citado 2025 nov. 23 ] Available from: https://doi.org/10.1007/s11548-019-01969-3
Vancouver
Ferreira Junior JR, Santos MK, Kikuchi Y, Faleiros MC, Cipriano FEG, Fabro AT, Yoshida H, Azevedo-Marques PM de. A radiomics approach for differentiation of pseudocavitation from cavitation on lung cancer tumors [Internet]. International Journal of Computer Assisted Radiology and Surgery. 2019 ; 14 S78.[citado 2025 nov. 23 ] Available from: https://doi.org/10.1007/s11548-019-01969-3
Trabalho de evento-resumo periodico
Deep learning-based radiomics of primary lung tumors in CT images for prediction of distant metastasis (2019)
Ferreira Junior, José Raniery; Santos, Marcel Koenigkam ; Hironaka, T.; Cipriano, Federico Enrique Garcia; Fabro, Alexandre Todorovic ; Yoshida, H.; Azevedo-Marques, Paulo Mazzoncini de
Fonte: International Journal of Computer Assisted Radiology and Surgery. Nome do evento: Computer Assisted Radiology and Surgery (CARS) - International Congress and Exhibition. Unidade: FMRP
Assuntos: RADIOMETRIA, NEOPLASIAS PULMONARES, CAVITAÇÃO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
FERREIRA JUNIOR, José Raniery et al. Deep learning-based radiomics of primary lung tumors in CT images for prediction of distant 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/s11548-019-01969-3. Acesso em: 23 nov. 2025. , 2019
APA
Ferreira Junior, J. R., Santos, M. K., Hironaka, T., Cipriano, F. E. G., Fabro, A. T., Yoshida, H., & Azevedo-Marques, P. M. de. (2019). Deep learning-based radiomics of primary lung tumors in CT images for prediction of distant metastasis. International Journal of Computer Assisted Radiology and Surgery. Heidelberg: Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo. doi:10.1007/s11548-019-01969-3
NLM
Ferreira Junior JR, Santos MK, Hironaka T, Cipriano FEG, Fabro AT, Yoshida H, Azevedo-Marques PM de. Deep learning-based radiomics of primary lung tumors in CT images for prediction of distant metastasis [Internet]. International Journal of Computer Assisted Radiology and Surgery. 2019 ; 14 S78-S79.[citado 2025 nov. 23 ] Available from: https://doi.org/10.1007/s11548-019-01969-3
Vancouver
Ferreira Junior JR, Santos MK, Hironaka T, Cipriano FEG, Fabro AT, Yoshida H, Azevedo-Marques PM de. Deep learning-based radiomics of primary lung tumors in CT images for prediction of distant metastasis [Internet]. International Journal of Computer Assisted Radiology and Surgery. 2019 ; 14 S78-S79.[citado 2025 nov. 23 ] Available from: https://doi.org/10.1007/s11548-019-01969-3
Trabalho de evento-resumo periodico
Computer-aided classification of inflammatory sacroiliitis in magnetic resonance imaging (2017)
Faleiros, M. Calil; Zavala, E. Jens Rivero; Ferreira Junior, J. Raniery; Dalton, V. Faeda; Assad, R. Luppino; Louzada Júnior, Paulo; Nogueira-Barbosa, Marcello Henrique; Azevedo-Marques, Paulo Mazzoncini de
Fonte: International Journal of Computer Assisted Radiology and Surgery. Nome do evento: Congress of Computer Assisted Radiology and Surgery (CARS). Unidade: FMRP
Assuntos: DIAGNÓSTICO POR COMPUTADOR, INFLAMAÇÃO, REUMATOLOGIA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
FALEIROS, M. Calil et al. Computer-aided classification of inflammatory sacroiliitis in magnetic resonance imaging. 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/s11548-017-1588-3. Acesso em: 23 nov. 2025. , 2017
APA
Faleiros, M. C., Zavala, E. J. R., Ferreira Junior, J. R., Dalton, V. F., Assad, R. L., Louzada Júnior, P., et al. (2017). Computer-aided classification of inflammatory sacroiliitis in magnetic resonance imaging. International Journal of Computer Assisted Radiology and Surgery. Heidelberg: Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo. doi:10.1007/s11548-017-1588-3
NLM
Faleiros MC, Zavala EJR, Ferreira Junior JR, Dalton VF, Assad RL, Louzada Júnior P, Nogueira-Barbosa MH, Azevedo-Marques PM de. Computer-aided classification of inflammatory sacroiliitis in magnetic resonance imaging [Internet]. International Journal of Computer Assisted Radiology and Surgery. 2017 ; 12 S154.[citado 2025 nov. 23 ] Available from: https://doi.org/10.1007/s11548-017-1588-3
Vancouver
Faleiros MC, Zavala EJR, Ferreira Junior JR, Dalton VF, Assad RL, Louzada Júnior P, Nogueira-Barbosa MH, Azevedo-Marques PM de. Computer-aided classification of inflammatory sacroiliitis in magnetic resonance imaging [Internet]. International Journal of Computer Assisted Radiology and Surgery. 2017 ; 12 S154.[citado 2025 nov. 23 ] Available from: https://doi.org/10.1007/s11548-017-1588-3
Artigo de periodico
Cooperative strategy for a dynamic ensemble of classification models in clinical applications: the case of MRI vertebral compression fractures (2017)
Casti, Paola; Mencattini, Arianna; Nogueira-Barbosa, Marcello Henrique; Pereira, Lucas Frighetto; Azevedo-Marques, Paulo Mazzoncini de; Martinelli, Eugenio; Di Natale, Corrado
Fonte: International Journal of Computer Assisted Radiology and Surgery. Unidade: FMRP
Assuntos: IMAGEM POR RESSONÂNCIA MAGNÉTICA, FRATURAS, COLUNA VERTEBRAL, REPRODUTIBILIDADE DE RESULTADOS, PROCESSAMENTO DE IMAGENS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
CASTI, Paola et al. Cooperative strategy for a dynamic ensemble of classification models in clinical applications: the case of MRI vertebral compression fractures. International Journal of Computer Assisted Radiology and Surgery, v. 12, n. 11, p. 1971-1983, 2017Tradução . . Disponível em: https://doi.org/10.1007/s11548-017-1625-2. Acesso em: 23 nov. 2025.
APA
Casti, P., Mencattini, A., Nogueira-Barbosa, M. H., Pereira, L. F., Azevedo-Marques, P. M. de, Martinelli, E., & Di Natale, C. (2017). Cooperative strategy for a dynamic ensemble of classification models in clinical applications: the case of MRI vertebral compression fractures. International Journal of Computer Assisted Radiology and Surgery, 12( 11), 1971-1983. doi:10.1007/s11548-017-1625-2
NLM
Casti P, Mencattini A, Nogueira-Barbosa MH, Pereira LF, Azevedo-Marques PM de, Martinelli E, Di Natale C. Cooperative strategy for a dynamic ensemble of classification models in clinical applications: the case of MRI vertebral compression fractures [Internet]. International Journal of Computer Assisted Radiology and Surgery. 2017 ; 12( 11): 1971-1983.[citado 2025 nov. 23 ] Available from: https://doi.org/10.1007/s11548-017-1625-2
Vancouver
Casti P, Mencattini A, Nogueira-Barbosa MH, Pereira LF, Azevedo-Marques PM de, Martinelli E, Di Natale C. Cooperative strategy for a dynamic ensemble of classification models in clinical applications: the case of MRI vertebral compression fractures [Internet]. International Journal of Computer Assisted Radiology and Surgery. 2017 ; 12( 11): 1971-1983.[citado 2025 nov. 23 ] Available from: https://doi.org/10.1007/s11548-017-1625-2
Artigo de periodico
Selecting relevant 3D image features of margin sharpness and texture for lung nodule retrieval (2017)
Ferreira Júnior, José Raniery; Azevedo-Marques, Paulo Mazzoncini de; Oliveira, Marcelo Costa
Fonte: International Journal of Computer Assisted Radiology and Surgery. Unidade: FMRP
Assuntos: NEOPLASIAS PULMONARES, TOMOGRAFIA COMPUTADORIZADA POR RAIOS X, SISTEMAS COMPUTADORIZADOS DE REGISTROS MÉDICOS, DIAGNÓSTICO POR IMAGEM
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
FERREIRA JÚNIOR, José Raniery e AZEVEDO-MARQUES, Paulo Mazzoncini de e OLIVEIRA, Marcelo Costa. Selecting relevant 3D image features of margin sharpness and texture for lung nodule retrieval. International Journal of Computer Assisted Radiology and Surgery, v. 12, n. 3, p. 509-517, 2017Tradução . . Disponível em: https://doi.org/10.1007/s11548-016-1471-7. Acesso em: 23 nov. 2025.
APA
Ferreira Júnior, J. R., Azevedo-Marques, P. M. de, & Oliveira, M. C. (2017). Selecting relevant 3D image features of margin sharpness and texture for lung nodule retrieval. International Journal of Computer Assisted Radiology and Surgery, 12( 3), 509-517. doi:10.1007/s11548-016-1471-7
NLM
Ferreira Júnior JR, Azevedo-Marques PM de, Oliveira MC. Selecting relevant 3D image features of margin sharpness and texture for lung nodule retrieval [Internet]. International Journal of Computer Assisted Radiology and Surgery. 2017 ; 12( 3): 509-517.[citado 2025 nov. 23 ] Available from: https://doi.org/10.1007/s11548-016-1471-7
Vancouver
Ferreira Júnior JR, Azevedo-Marques PM de, Oliveira MC. Selecting relevant 3D image features of margin sharpness and texture for lung nodule retrieval [Internet]. International Journal of Computer Assisted Radiology and Surgery. 2017 ; 12( 3): 509-517.[citado 2025 nov. 23 ] Available from: https://doi.org/10.1007/s11548-016-1471-7
Trabalho de evento-resumo periodico
Multi-segmentation and cooperative classification models improve the identification of malignant vertebral compression fractures in MRI (2017)
Casti, P.; Mencattini, A.; Nogueira-Barbosa, Marcello Henrique; Frighetto-Pereira, L; Azevedo-Marques, Paulo Mazzoncini de; Martinelli, E.; Di Natale, C.
Fonte: International Journal of Computer Assisted Radiology and Surgery. Nome do evento: Congress of Computer Assisted Radiology and Surgery (CARS). Unidade: FMRP
Assunto: RECONHECIMENTO DE PADRÕES
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
CASTI, P. et al. Multi-segmentation and cooperative classification models improve the identification of malignant vertebral compression fractures in MRI. 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/s11548-017-1588-3. Acesso em: 23 nov. 2025. , 2017
APA
Casti, P., Mencattini, A., Nogueira-Barbosa, M. H., Frighetto-Pereira, L., Azevedo-Marques, P. M. de, Martinelli, E., & Di Natale, C. (2017). Multi-segmentation and cooperative classification models improve the identification of malignant vertebral compression fractures in MRI. International Journal of Computer Assisted Radiology and Surgery. Heidelberg: Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo. doi:10.1007/s11548-017-1588-3
NLM
Casti P, Mencattini A, Nogueira-Barbosa MH, Frighetto-Pereira L, Azevedo-Marques PM de, Martinelli E, Di Natale C. Multi-segmentation and cooperative classification models improve the identification of malignant vertebral compression fractures in MRI [Internet]. International Journal of Computer Assisted Radiology and Surgery. 2017 ; 12 S153.[citado 2025 nov. 23 ] Available from: https://doi.org/10.1007/s11548-017-1588-3
Vancouver
Casti P, Mencattini A, Nogueira-Barbosa MH, Frighetto-Pereira L, Azevedo-Marques PM de, Martinelli E, Di Natale C. Multi-segmentation and cooperative classification models improve the identification of malignant vertebral compression fractures in MRI [Internet]. International Journal of Computer Assisted Radiology and Surgery. 2017 ; 12 S153.[citado 2025 nov. 23 ] Available from: https://doi.org/10.1007/s11548-017-1588-3
Trabalho de evento-resumo periodico
Recognition of vertebral compression fractures in magnetic resonance images using measures of distances to the centroid (2016)
Frighetto-Pereira, L.; Rangayyan, R. M.; Metzner, G. A.; Azevedo-Marques, Paulo Mazzoncini de; Nogueira-Barbosa, Marcello Henrique
Fonte: International Journal of Computer Assisted Radiology and Surgery. Nome do evento: Computer Assisted Radiology and Surgery (CARS). Unidade: FMRP
Assuntos: COLUNA VERTEBRAL, IMAGEM POR RESSONÂNCIA MAGNÉTICA, FRATURAS, IDOSOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
FRIGHETTO-PEREIRA, L. et al. Recognition of vertebral compression fractures in magnetic resonance images using measures of distances to the centroid. International Journal of Computer Assisted Radiology and Surgery. Heidelberg: Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo. . Acesso em: 23 nov. 2025. , 2016
APA
Frighetto-Pereira, L., Rangayyan, R. M., Metzner, G. A., Azevedo-Marques, P. M. de, & Nogueira-Barbosa, M. H. (2016). Recognition of vertebral compression fractures in magnetic resonance images using measures of distances to the centroid. International Journal of Computer Assisted Radiology and Surgery. Heidelberg: Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo.
NLM
Frighetto-Pereira L, Rangayyan RM, Metzner GA, Azevedo-Marques PM de, Nogueira-Barbosa MH. Recognition of vertebral compression fractures in magnetic resonance images using measures of distances to the centroid. International Journal of Computer Assisted Radiology and Surgery. 2016 ; 11 S123-S124.[citado 2025 nov. 23 ]
Vancouver
Frighetto-Pereira L, Rangayyan RM, Metzner GA, Azevedo-Marques PM de, Nogueira-Barbosa MH. Recognition of vertebral compression fractures in magnetic resonance images using measures of distances to the centroid. International Journal of Computer Assisted Radiology and Surgery. 2016 ; 11 S123-S124.[citado 2025 nov. 23 ]
Trabalho de evento-resumo periodico
Pulmonary nodule classification with 3D features of texture and margin sharpness (2016)
Ferreira Júnior, J.; Oliveira, M. C.; Azevedo-Marques, Paulo Mazzoncini de
Fonte: International Journal of Computer Assisted Radiology and Surgery. Nome do evento: Computer Assisted Radiology and Surgery (CARS). Unidade: FMRP
Assuntos: PULMÃO, IMAGEM 3D, CLASSIFICAÇÃO DE DOENÇAS, TOMOGRAFIA COMPUTADORIZADA DE EMISSÃO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
FERREIRA JÚNIOR, J. e OLIVEIRA, M. C. e AZEVEDO-MARQUES, Paulo Mazzoncini de. Pulmonary nodule classification with 3D features of texture and margin sharpness. International Journal of Computer Assisted Radiology and Surgery. Heidelberg: Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo. . Acesso em: 23 nov. 2025. , 2016
APA
Ferreira Júnior, J., Oliveira, M. C., & Azevedo-Marques, P. M. de. (2016). Pulmonary nodule classification with 3D features of texture and margin sharpness. International Journal of Computer Assisted Radiology and Surgery. Heidelberg: Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo.
NLM
Ferreira Júnior J, Oliveira MC, Azevedo-Marques PM de. Pulmonary nodule classification with 3D features of texture and margin sharpness. International Journal of Computer Assisted Radiology and Surgery. 2016 ; 11 S272.[citado 2025 nov. 23 ]
Vancouver
Ferreira Júnior J, Oliveira MC, Azevedo-Marques PM de. Pulmonary nodule classification with 3D features of texture and margin sharpness. International Journal of Computer Assisted Radiology and Surgery. 2016 ; 11 S272.[citado 2025 nov. 23 ]
Trabalho de evento-resumo periodico
Pattern recognition of diffuse lung disease in HRCT for content-based image retrieval and computer-aided diagnosis (2013)
Calabrez, L.; Ponciano-Silva, M.; Azevedo-Marques, Paulo Mazzoncini de
Fonte: International Journal of Computer Assisted Radiology and Surgery. Nome do evento: International Congress and Exhibition on Computer Assisted Radiology and Surgery. Unidade: FMRP
Assuntos: DIAGNÓSTICO POR COMPUTADOR, IMAGEM DIGITAL (RECUPERAÇÃO), TOMOGRAFIA, DOENÇA PULMONAR (ESPECIALIDADE)
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
CALABREZ, L. e PONCIANO-SILVA, M. e AZEVEDO-MARQUES, Paulo Mazzoncini de. Pattern recognition of diffuse lung disease in HRCT for content-based image retrieval and computer-aided diagnosis. International Journal of Computer Assisted Radiology and Surgery. Heidelberg: Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo. . Acesso em: 23 nov. 2025. , 2013
APA
Calabrez, L., Ponciano-Silva, M., & Azevedo-Marques, P. M. de. (2013). Pattern recognition of diffuse lung disease in HRCT for content-based image retrieval and computer-aided diagnosis. International Journal of Computer Assisted Radiology and Surgery. Heidelberg: Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo.
NLM
Calabrez L, Ponciano-Silva M, Azevedo-Marques PM de. Pattern recognition of diffuse lung disease in HRCT for content-based image retrieval and computer-aided diagnosis. International Journal of Computer Assisted Radiology and Surgery. 2013 ;[citado 2025 nov. 23 ]
Vancouver
Calabrez L, Ponciano-Silva M, Azevedo-Marques PM de. Pattern recognition of diffuse lung disease in HRCT for content-based image retrieval and computer-aided diagnosis. International Journal of Computer Assisted Radiology and Surgery. 2013 ;[citado 2025 nov. 23 ]
Artigo de periodico
Automated characterization of secondary signals of breast cancer to compose a module from a CADx scheme (2013)
Schiabel, Homero ; Menechelli, Renan Caldeira
Fonte: International Journal of Computer Assisted Radiology and Surgery. Unidade: EESC
Assuntos: MAMOGRAFIA, CAD, NEOPLASIAS MAMÁRIAS, DIAGNÓSTICO POR COMPUTADOR
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SCHIABEL, Homero e MENECHELLI, Renan Caldeira. Automated characterization of secondary signals of breast cancer to compose a module from a CADx scheme. International Journal of Computer Assisted Radiology and Surgery, v. 8, n. 1 supplement, p. S404-S405, 2013Tradução . . Disponível em: https://doi.org/10.1007/s11548-013-0883-x. Acesso em: 23 nov. 2025.
APA
Schiabel, H., & Menechelli, R. C. (2013). Automated characterization of secondary signals of breast cancer to compose a module from a CADx scheme. International Journal of Computer Assisted Radiology and Surgery, 8( 1 supplement), S404-S405. doi:10.1007/s11548-013-0883-x
NLM
Schiabel H, Menechelli RC. Automated characterization of secondary signals of breast cancer to compose a module from a CADx scheme [Internet]. International Journal of Computer Assisted Radiology and Surgery. 2013 ; 8( 1 supplement): S404-S405.[citado 2025 nov. 23 ] Available from: https://doi.org/10.1007/s11548-013-0883-x
Vancouver
Schiabel H, Menechelli RC. Automated characterization of secondary signals of breast cancer to compose a module from a CADx scheme [Internet]. International Journal of Computer Assisted Radiology and Surgery. 2013 ; 8( 1 supplement): S404-S405.[citado 2025 nov. 23 ] Available from: https://doi.org/10.1007/s11548-013-0883-x

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