AI-based radiomic approach in high-resolution CT images for differential diagnosis of idiopathic pulmonary fibrosis (2020)
- Authors:
- USP affiliated authors: SANTOS, MARCEL KOENIGKAM - FMRP ; FABRO, ALEXANDRE TODOROVIC - FMRP ; MARQUES, PAULO MAZZONCINI DE AZEVEDO - FMRP ; DORILÊO, EDERSON ANTONIO GOMES - FMRP
- Unidade: FMRP
- DOI: 10.1007/s11548-020-02171-6
- Subjects: FIBROSE PULMONAR; BIOMARCADORES; INTELIGÊNCIA ARTIFICIAL; RADIOLOGIA; DIAGNÓSTICO POR COMPUTADOR
- Keywords: Radiomics; Idiopathic pulmonary fibrosis; Interstitial lung disease; High resolution CT
- 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. S120-S121, 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
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: 28 dez. 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 dez. 28 ] 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 dez. 28 ] Available from: https://doi.org/10.1007/s11548-020-02171-6 - Towards convolutional neural network on primary lung tumors to predict histophatological type, distant and lymph node metastasis
- 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
- Indexação e recuperação por conteúdo em imagens de úlceras cutâneas
- 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
Informações sobre o DOI: 10.1007/s11548-020-02171-6 (Fonte: oaDOI API)
Download do texto completo
| Tipo | Nome | Link | |
|---|---|---|---|
 |
003079743.pdf |
How to cite
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
