Portal de investigação
Multiple instance learning for lung pathophysiological findings detection using CT scans
Autores da FMUP
Participantes de fora da FMUP
- Frade, J
- Pereira, T
- Morgado, J
- Silva, F
- Mendes, J
- Negrao, E
- de Lima, BF
- da Silva, MC
- Madureira, A.
- Ramos, I
- Costa, JL
- Cunha, A
- Oliveira, HP
Unidades de investigação
Abstract
Lung diseases affect the lives of billions of people worldwide, and 4 million people, each year, die prematurely due to this condition. These pathologies are characterized by specific imagiological findings in CT scans. The traditional Computer-Aided Diagnosis (CAD) approaches have been showing promising results to help clinicians; however, CADs normally consider a small part of the medical image for analysis, excluding possible relevant information for clinical evaluation. Multiple Instance Learning (MIL) approach takes into consideration different small pieces that are relevant for the final classification and creates a comprehensive analysis of pathophysiological changes. This study uses MIL-based approaches to identify the presence of lung pathophysiological findings in CT scans for the characterization of lung disease development. This work was focus on the detection of the following: Fibrosis, Emphysema, Satellite Nodules in Primary Lesion Lobe, Nodules in Contralateral Lung and Ground Glass, being Fibrosis and Emphysema the ones with more outstanding results, reaching an Area Under the Curve (AUC) of 0.89 and 0.72, respectively. Additionally, the MIL-based approach was used for EGFR mutation status prediction - the most relevant oncogene on lung cancer, with an AUC of 0.69. The results showed that this comprehensive approach can be a useful tool for lung pathophysiological characterization.
Dados da publicação
- ISSN/ISSNe:
- 1741-0444, 0140-0118
- Tipo:
- Article
- Páginas:
- 1569-1584
- Link para outro recurso:
- www.scopus.com
MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING Springer Verlag
Citações Recebidas na Web of Science: 1
Citações Recebidas na Scopus: 8
Documentos
- Não há documentos
Filiações
Keywords
- Multiple instance learning; Computer-aided diagnosis; Computed tomography; Lung disease detection; Lung cancer characterization
Financiamento
Proyectos asociados
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Investigador Principal: Venceslau José Coelho Pinto Hespanhol
Estudo Clínico Académico (Refluxo Gastro-esofágic) . 2020
A importância do diagnóstico na Fibrose Pulmonar Idiopática
Investigador Principal: Venceslau José Coelho Pinto Hespanhol
Estudo Clínico Académico (FPI) . 2020
Imunoterapia no cancro do pulmão: PD-L1, biomarcador preditivo?
Investigador Principal: Venceslau José Coelho Pinto Hespanhol
Estudo Clínico Académico . 2020
Citar a publicação
Frade J,Pereira T,Morgado J,Silva F,Freitas C,Mendes J,Negrao E,de Lima BF,da MC,Madureira A,Ramos I,Costa JL,Hespanhol V,Cunha A,Oliveira HP. Multiple instance learning for lung pathophysiological findings detection using CT scans. Med. Biol. Eng. Comput. 2022. 60. (6):p. 1569-1584. IF:3,200. (2).
