Towards convolutional neural network on primary lung tumors to predict histophatological type, distant and lymph node metastasis (2020)
- Authors:
- USP affiliated authors: FABRO, ALEXANDRE TODOROVIC - FMRP ; SANTOS, MARCEL KOENIGKAM - FMRP ; MARQUES, PAULO MAZZONCINI DE AZEVEDO - FMRP
- Unidade: FMRP
- DOI: 10.1007/s11548-020-02171-6
- Subjects: NEOPLASIAS PULMONARES; METÁSTASE ANIMAL; REDES NEURAIS; DIAGNÓSTICO POR COMPUTADOR; APRENDIZADO COMPUTACIONAL
- Keywords: Lung cancer; Distant metastasis; Lymph node metastasis; Convolutional neural networks
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher place: Heidelberg
- Date published: 2020
- Source:
- Título: International Journal of Computer Assisted Radiology and Surgery
- ISSN: 1861-6410
- Volume/Número/Paginação/Ano: v. 15, suppl. 1, p. S116-S117, 2020
- Conference titles: International Congress and Exhibition on Computer Assisted Radiology and Surgery - CARS
- Este periódico é de assinatura
- Este artigo é de acesso aberto
- URL de acesso aberto
- Cor do Acesso Aberto: bronze
-
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: 28 dez. 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 dez. 28 ] 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 dez. 28 ] Available from: https://doi.org/10.1007/s11548-020-02171-6 - AI-based radiomic approach in high-resolution CT images for differential diagnosis of idiopathic pulmonary fibrosis
- Radiomics-based features for pattern recognition of lung cancer histopathology and metastases
- A radiomics approach for differentiation of pseudocavitation from cavitation on lung cancer tumors
- Deep learning-based radiomics of primary lung tumors in CT images for prediction of distant metastasis
- Quantifying intratumor heterogeneity of lung neoplasms with radiomics
- Radiomic analysis of lung cancer for the assessment of patient prognosis and intratumor heterogeneity
- Application of Convolutional Neural Network with Transfer learning to pattern recognition of tuberculosis in chest X-ray images
- CT-based radiomics for prediction of histologic subtype and metastatic disease in primary malignant lung neoplasms
- Alterações neurorradiológicas em pacientes com síndrome de Kallmann: estudos por ressonância magnética
- Correlations between volumetric capnography and automated quantitative computed tomography analysis in patients with severe COPD
Informações sobre o DOI: 10.1007/s11548-020-02171-6 (Fonte: oaDOI API)
Download do texto completo
| Tipo | Nome | Link | |
|---|---|---|---|
 |
003079720.pdf |
How to cite
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
