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

Author(s)

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

• Document type: Journal article
• Source: NEURAL COMPUTING & APPLICATIONS; v. N/A, p. 15-pg., 2024-03-14.

Abstract

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)

• FAPESP's process: 17/04847-9 - Barrett's Esophagus Assisted Diagnosis Using Machine Learning
• Grantee: Luis Antonio de Souza Júnior
• Support Opportunities: Scholarships in Brazil - Doctorate
• FAPESP's process: 19/08605-5 - Computer-assisted diagnosis of Barretts's esophagus using machine learning techniques
• Grantee: Luis Antonio de Souza Júnior
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate
• FAPESP's process: 16/19403-6 - Energy-based learning models and their applications
• Grantee: João Paulo Papa
• Support Opportunities: Regular Research Grants
• FAPESP's process: 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction?
• Grantee: Alexandre Xavier Falcão
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
• Grantee: Francisco Louzada Neto
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC