| Full text | |
| Author(s): |
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
Total Authors: 10
|
| Document type: | Journal article |
| Source: | MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING; v. 62, n. 11, p. 18-pg., 2024-06-07. |
| Abstract | |
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) | |
| 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 |