Layer-selective deep representation to improve esophageal cancer classification

  • 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.

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Author:Luis Antonio de Souza Jr.ORCiD, Leandro A. Passos, Marcos Cleison S. Santana, Robert MendelORCiD, David Rauber, Alanna EbigboORCiD, Andreas Probst, Helmut Messmann, João Paulo PapaORCiD, Christoph PalmORCiDGND
DOI:https://doi.org/10.1007/s11517-024-03142-8
Parent Title (English):Medical & Biological Engineering & Computing
Publisher:Springer Nature
Place of publication:Heidelberg
Document Type:Article
Language:English
Year of first Publication:2024
Release Date:2024/06/13
Tag:Barrett’s esophagus detection; Convolutional neural networks; Deep learning; Multistep training
Pagenumber:18
Institutes:Fakultät Informatik und Mathematik
Fakultät Informatik und Mathematik / Regensburg Medical Image Computing (ReMIC)
Begutachtungsstatus:peer-reviewed
research focus:Lebenswissenschaften und Ethik
Licence (German):Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG