CXAI: Explaining Convolutional Neural Networks for Medical Imaging Diagnostic

Deep learning models have been increasingly applied to medical images for tasks such as lesion detection, segmentation, and diagnosis. However, the field suffers from the lack of concrete definitions for usable explanations in different settings. To identify specific aspects of explainability that m...

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Bibliographic Details
Published in:Electronics (Basel) 2022-06, Vol.11 (11), p.1775
Main Authors: Rguibi, Zakaria, Hajami, Abdelmajid, Zitouni, Dya, Elqaraoui, Amine, Bedraoui, Anas
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Artificial neural networks
Decision making
Deep learning
Image analysis
Image segmentation
Medical imaging
Neural networks
Nurses
Nursing care
Physicians
Professionals
Trustworthiness
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Summary:Deep learning models have been increasingly applied to medical images for tasks such as lesion detection, segmentation, and diagnosis. However, the field suffers from the lack of concrete definitions for usable explanations in different settings. To identify specific aspects of explainability that may catalyse building trust in deep learning models, we will use some techniques to demonstrate many aspects of explaining convolutional neural networks in a medical imaging context. One important factor influencing clinician’s trust is how well a model can justify its predictions or outcomes. Clinicians need understandable explanations about why a machine-learned prediction was made so they can assess whether it is accurate and clinically useful. The provision of appropriate explanations has been generally understood to be critical for establishing trust in deep learning models. However, there lacks a clear understanding on what constitutes an explanation that is both understandable and useful across different domains such as medical image analysis, which hampers efforts towards developing explanatory tool sets specifically tailored towards these tasks. In this paper, we investigated two major directions for explaining convolutional neural networks: feature-based post hoc explanatory methods that try to explain already trained and fixed target models and preliminary analysis and choice of the model architecture with an accuracy of 98% ± 0.156% from 36 CNN architectures with different configurations.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics11111775