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Convolutional Neural Networks for the evaluation of cancer in Barrett’s esophagus: Explainable AI to lighten up the black-box

  • Even though artificial intelligence and machine learning have demonstrated remarkable performances in medical image computing, their level of accountability and transparency must be provided in such evaluations. The reliability related to machine learning predictions must be explained and interpreted, especially if diagnosis support is addressed. For this task, the black-box nature of deep learning techniques must be lightened up to transfer its promising results into clinical practice. Hence, we aim to investigate the use of explainable artificial intelligence techniques to quantitatively highlight discriminative regions during the classification of earlycancerous tissues in Barrett’s esophagus-diagnosed patients. Four Convolutional Neural Network models (AlexNet, SqueezeNet, ResNet50, and VGG16) were analyzed using five different interpretation techniques (saliency, guided backpropagation, integrated gradients, input × gradients, and DeepLIFT) to compare their agreement with experts’ previous annotations of cancerous tissue. We could show that saliency attributes match best with the manual experts’ delineations. Moreover, there is moderate to high correlation between the sensitivity of a model and the human-and-computer agreement. The results also lightened that the higher the model’s sensitivity, the stronger the correlation of human and computational segmentation agreement. We observed a relevant relation between computational learning and experts’ insights, demonstrating how human knowledge may influence the correct computational learning.

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Verfasserangaben:Luis Antonio De Souza Jr.ORCiD, Robert MendelORCiD, Sophia Strasser, Alanna EbigboORCiD, Andreas Probst, Helmut Messmann, João Paulo PapaORCiD, Christoph PalmORCiDGND
URN:urn:nbn:de:bvb:898-opus4-20126
DOI:https://doi.org/10.1016/j.compbiomed.2021.104578
ISSN:0010-4825
Titel des übergeordneten Werkes (Englisch):Computers in Biology and Medicine
Verlag:Elsevier
Dokumentart:Artikel aus einer Zeitschrift/Zeitung
Sprache der Veröffentlichung:Englisch
Jahr der Veröffentlichung:2021
Veröffentlichende Institution:Ostbayerische Technische Hochschule Regensburg
Datum der Freischaltung:25.06.2021
Freies Schlagwort / Tag:Adenocarcinoma; Barrett's esophagus; Computer-aided diagnosis; Explainable artificial intelligence; Machine learning
GND-Schlagwort:Deep Learning; Künstliche Intelligenz; Computerunterstützte Medizin
Jahrgang:135
Aufsatznummer:104578
Erste Seite:1
Letzte Seite:14
Fakultäten / Institute / Einrichtungen:Fakultät Informatik und Mathematik
Regensburg Center of Health Sciences and Technology - RCHST
Fakultät Informatik und Mathematik / Regensburg Medical Image Computing (ReMIC)
Begutachtungsstatus:peer-reviewed
OpenAccess Publikationsweg:Gold Open Access- Erstveröffentlichung in einem/als Open-Access-Medium
Forschungsschwerpunkt:Lebenswissenschaften und Ethik
Lizenz (Deutsch):Creative Commons - CC BY - Namensnennung 4.0 International