| Texto completo | |
| Autor(es): |
Souza, Luis A.
;
Passos, Leandro A.
;
Santana, Marcos Cleison S.
;
Mendel, Robert
;
Rauber, David
;
Ebigbo, Alanna
;
Probst, Andreas
;
Messmann, Helmut
;
Papa, Joao Paulo
;
Palm, Christoph
Número total de Autores: 10
|
| Tipo de documento: | Artigo Científico |
| Fonte: | MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING; v. 62, n. 11, p. 18-pg., 2024-06-07. |
| Resumo | |
Even though artificial intelligence and machine learning have demonstrated remarkable performances in medical image computing, their accountability and transparency level must be improved to transfer this success into clinical practice. The reliability of machine learning decisions must be explained and interpreted, especially for supporting the medical diagnosis. For this task, the deep learning techniques' black-box nature must somehow be lightened up to clarify its promising results. Hence, we aim to investigate the impact of the ResNet-50 deep convolutional design for Barrett's esophagus and adenocarcinoma classification. For such a task, and aiming at proposing a two-step learning technique, the output of each convolutional layer that composes the ResNet-50 architecture was trained and classified for further definition of layers that would provide more impact in the architecture. We showed that local information and high-dimensional features are essential to improve the classification for our task. Besides, we observed a significant improvement when the most discriminative layers expressed more impact in the training and classification of ResNet-50 for Barrett's esophagus and adenocarcinoma classification, demonstrating that both human knowledge and computational processing may influence the correct learning of such a problem. (AU) | |
| Processo FAPESP: | 17/04847-9 - Auxílio ao Diagnóstico Automático do Esôfago de Barrett Utilizando Aprendizado de Máquina |
| Beneficiário: | Luis Antonio de Souza Júnior |
| Modalidade de apoio: | Bolsas no Brasil - Doutorado |
| Processo FAPESP: | 19/08605-5 - Diagnóstico do esôfago de Barrett auxiliado por computador utilizando técnicas de aprendizado de máquina |
| Beneficiário: | Luis Antonio de Souza Júnior |
| Modalidade de apoio: | Bolsas no Exterior - Estágio de Pesquisa - Doutorado |
| Processo FAPESP: | 16/19403-6 - Modelos de aprendizado baseados em energia e suas aplicações |
| Beneficiário: | João Paulo Papa |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |
| Processo FAPESP: | 14/12236-1 - AnImaLS: Anotação de Imagem em Larga Escala: o que máquinas e especialistas podem aprender interagindo? |
| Beneficiário: | Alexandre Xavier Falcão |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |
| Processo FAPESP: | 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria |
| Beneficiário: | Francisco Louzada Neto |
| Modalidade de apoio: | Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs |