ArticleSensors (Basel, Switzerland)2021
Explainable Artificial Intelligence for Bias Detection in COVID CT-Scan Classifiers.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects.Diagnostic and interventional radiology (Ankara, Turkey) · 2025Review
- Open your black box classifier.Healthcare technology letters · 2024Article
- Artificial Intelligence and Infectious Disease Imaging.The Journal of infectious diseases · 2023Article
- Essential properties and explanation effectiveness of explainable artificial intelligence in healthcare: A systematic review.Heliyon · 2023Review
- Explainable artificial intelligence (XAI) in radiology and nuclear medicine: a literature review.Frontiers in medicine · 2023Review
- Fairness-related performance and explainability effects in deep learning models for brain image analysis.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- Explaining One-Dimensional Convolutional Models in Human Activity Recognition and Biometric Identification Tasks.Sensors (Basel, Switzerland) · 2022Article
- Validating Automatic Concept-Based Explanations for AI-Based Digital Histopathology.Sensors (Basel, Switzerland) · 2022Article
- COVID-19 Classification from Chest X-Ray Images: A Framework of Deep Explainable Artificial Intelligence.Computational intelligence and neuroscience · 2022Article
- Using deep-learning in fetal ultrasound analysis for diagnosis of cystic hygroma in the first trimester.PloS one · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
problemAn application of Explainable Artificial Intelligence Methods for COVID CT-Scan classifiers is presented. MOTIVATION: It is possible that classifiers are using spurious artifacts in dataset images to achieve high performances, and such explainable techniques can help identify this issue.
aimFor this purpose, several approaches were used in tandem, in order to create a complete overview of the classificatios. METHODOLOGY: The techniques used included GradCAM, LIME, RISE, Squaregrid, and direct Gradient approaches (Vanilla, Smooth, Integrated). MAIN
resultsAmong the deep neural networks architectures evaluated for this image classification task, VGG16 was shown to be most affected by biases towards spurious artifacts, while DenseNet was notably more robust against them. Further impacts: Results further show that small differences in validation accuracies can cause drastic changes in explanation heatmaps for DenseNet architectures, indicating that small changes in validation accuracy may have large impacts on the biases learned by the networks. Notably, it is important to notice that the strong performance metrics achieved by all these networks (Accuracy, F1 score, AUC all in the 80 to 90% range) could give users the erroneous impression that there is no bias. However, the analysis of the explanation heatmaps highlights the bias.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.