DeepCraftFuse: visual and deeply-learnable features work better together for esophageal cancer detection in patients with Barrett's esophagus

Autor(es)

Souza Jr, Luis A. ; Pacheco, Andre G. C. ; Passos, Leandro A. ; Santana, Marcos C. S. ; Mendel, Robert ; Ebigbo, Alanna ; Probst, Andreas ; Messmann, Helmut ; Palm, Christoph ; Papa, Joao Paulo

• Tipo de documento: Artigo Científico
• Fonte: NEURAL COMPUTING & APPLICATIONS; v. N/A, p. 15-pg., 2024-03-14.

Resumo

Limitations in computer-assisted diagnosis include lack of labeled data and inability to model the relation between what experts see and what computers learn. 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. While deep learning techniques are broad so that unseen information might help learn patterns of interest, human insights to describe objects of interest help in decision-making. This paper proposes a novel approach, DeepCraftFuse, to address the challenge of combining information provided by deep networks with visual-based features to significantly enhance the correct identification of cancerous tissues in patients affected with Barrett's esophagus (BE). We demonstrate that DeepCraftFuse outperforms state-of-the-art techniques on private and public datasets, reaching results of around 95% when distinguishing patients affected by BE that is either positive or negative to esophageal cancer. (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