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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Metadaten
Author:Luis A. de Souza Jr., Robert Mendel, Sophia Strasser, Alanna EbigboORCiD, Andreas Probst, Helmut Messmann, João P. PapaORCiD, Christoph PalmORCiDGND
DOI:https://doi.org/10.1016/j.compbiomed.2021.104578
ISSN:0010-4825
Parent Title (English):Computers in Biology and Medicine
Publisher:Elsevier
Document Type:Article
Language:English
Year of first Publication:2021
Release Date:2021/06/25
Tag:Adenocarcinoma; Barrett's esophagus; Computer-aided diagnosis; Explainable artificial intelligence; Machine learning
GND Keyword:Deep Learning; Künstliche Intelligenz; Computerunterstützte Medizin
Volume:135
Article Number:104578
Pagenumber:14
Institutes:Fakultät Informatik und Mathematik
Regensburg Center of Health Sciences and Technology - RCHST
Regensburg Medical Image Computing - ReMIC
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke
6 Technik, Medizin, angewandte Wissenschaften
Begutachtungsstatus:peer-reviewed
OpenAccess Publikationsweg:Erstveröffentlichungen
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International