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

  • 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.

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Author:Luis Antonio de Souza Jr.ORCiD, André G.C. Pacheco, Leandro A. Passos, Marcos C. S. Santana, Robert MendelORCiD, Alanna EbigboORCiD, Andreas Probst, Helmut Messmann, Christoph PalmORCiDGND, João Paulo PapaORCiD
Parent Title (English):Neural Computing and Applications
Place of publication:London
Document Type:Article
Year of first Publication:2024
Release Date:2024/03/22
Tag:Adenocarcinoma; Barrett’s esophagus; Deep Learning; Machine learning; Object detector
GND Keyword:Deep Learning; Speiseröhrenkrebs; Adenocarcinom; Endobrachyösophagus; Diagnose; Maschinelles Lernen
First Page:10445
Last Page:10459
Institutes:Fakultät Informatik und Mathematik
Fakultät Informatik und Mathematik / Regensburg Medical Image Computing (ReMIC)
Kostenträger (Forschungsprojekt, Labor, Einrichtung etc.):2027700
research focus:Lebenswissenschaften und Ethik
Licence (German):Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG
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