Explainable Artificial Intelligence for Bias Detection in COVID CT-Scan Classifiers

Sousa, Iam Palatnik de; Vellasco, Marley; Silva, Eduardo Costa da

doi:10.3390/s21165657

Cited by 27 publications

(10 citation statements)

References 20 publications

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“…The Gradient-weighted Class Activation Mapping (Grad-CAM) method was used to improve the interpretability of our trained DenseNet models, and visually contextualize important features in the image data that were used for model predictions [ 20 ]. Grad-CAM is a widely used technique for the visual explanation of deep-learning algorithms [ 21 , 22 ]. With this approach, heat maps were generated from 8x8 feature maps to highlight regions of each image that were the most important for model prediction ( Fig 4 ).…”

Section: Methodsmentioning

confidence: 99%

Using deep-learning in fetal ultrasound analysis for diagnosis of cystic hygroma in the first trimester

et al. 2022

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Objective To develop and internally validate a deep-learning algorithm from fetal ultrasound images for the diagnosis of cystic hygromas in the first trimester. Methods All first trimester ultrasound scans with a diagnosis of a cystic hygroma between 11 and 14 weeks gestation at our tertiary care centre in Ontario, Canada were studied. Ultrasound scans with normal nuchal translucency were used as controls. The dataset was partitioned with 75% of images used for model training and 25% used for model validation. Images were analyzed using a DenseNet model and the accuracy of the trained model to correctly identify cases of cystic hygroma was assessed by calculating sensitivity, specificity, and the area under the receiver-operating characteristic (ROC) curve. Gradient class activation heat maps (Grad-CAM) were generated to assess model interpretability. Results The dataset included 289 sagittal fetal ultrasound images;129 cystic hygroma cases and 160 normal NT controls. Overall model accuracy was 93% (95% CI: 88–98%), sensitivity 92% (95% CI: 79–100%), specificity 94% (95% CI: 91–96%), and the area under the ROC curve 0.94 (95% CI: 0.89–1.0). Grad-CAM heat maps demonstrated that the model predictions were driven primarily by the fetal posterior cervical area. Conclusions Our findings demonstrate that deep-learning algorithms can achieve high accuracy in diagnostic interpretation of cystic hygroma in the first trimester, validated against expert clinical assessment.

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Section: Methodsmentioning

confidence: 99%

Using deep-learning in fetal ultrasound analysis for diagnosis of cystic hygroma in the first trimester

et al. 2022

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“…Rahman et al showed that many COVID-19 diagnostic models are vulnerable to attacks by adversarial examples [20]. Palatnik de Sousa et al [21] also demonstrated the utility of adding random colored artifacts to CT images to identify model architecture which are most robust to such perturbation. This illustrates the importance of robust validation of models prior to their integration within clinical settings.…”

Section: Identificationmentioning

confidence: 99%

“…Data Augmentation Latent Space Interpretation Intrinsic latent space guidance [15]- [18], [20], [21] Post-hoc PCA-based [19] Outcome Prediction…”

Section: Strategy Category Techniquementioning

confidence: 99%

Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review

Giuste

Shi

Zhu

et al. 2023

IEEE Rev. Biomed. Eng.

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Despite the myriad peer-reviewed papers demonstrating novel Artificial Intelligence (AI)-based solutions to COVID-19 challenges during the pandemic, few have made a significant clinical impact, especially in diagnosis and disease precision staging. One major cause for such low impact is the lack of model transparency, significantly limiting the AI adoption in real clinical practice. To solve this problem, AI models need to be explained to users. Thus, we have conducted a comprehensive study of Explainable Artificial Intelligence (XAI) using PRISMA technology. Our findings suggest that XAI can improve model performance, instill trust in the users, and assist users in decisionmaking. In this systematic review, we introduce common XAI techniques and their utility with specific examples of their application. We discuss the evaluation of XAI results because it is an important step for maximizing the value of AI-based clinical decision support systems. Additionally, we present the traditional, modern, and advanced XAI models to demonstrate the evolution of novel techniques. Finally, we provide a best practice guideline that developers can refer to during the model experimentation. We also offer potential solutions with specific examples for common challenges in AI model experimentation. This comprehensive review, hopefully, can promote AI adoption in biomedicine and healthcare.

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“…eir dataset included 166 individuals' CT scans, 72 of them were COVID positive, and 35 were interstitial but COVID negative. Similarly, Palatnik et al [37] identified the issue of usage of false or doubtful artifacts in dataset images by classifiers; they suggested a new AI-based technique for COVID-19 classification on the COVID-CT dataset [38].…”

Section: Viral Pneumonia Normalmentioning

confidence: 99%

COVID-19 Classification from Chest X-Ray Images: A Framework of Deep Explainable Artificial Intelligence

Khan

Azhar

Ibrar

et al. 2022

Computational Intelligence and Neuroscience

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COVID-19 detection and classification using chest X-ray images is a current hot research topic based on the important application known as medical image analysis. To halt the spread of COVID-19, it is critical to identify the infection as soon as possible. Due to time constraints and the expertise of radiologists, manually diagnosing this infection from chest X-ray images is a difficult and time-consuming process. Artificial intelligence techniques have had a significant impact on medical image analysis and have also introduced several techniques for COVID-19 diagnosis. Deep learning and explainable AI have shown significant popularity among AL techniques for COVID-19 detection and classification. In this work, we propose a deep learning and explainable AI technique for the diagnosis and classification of COVID-19 using chest X-ray images. Initially, a hybrid contrast enhancement technique is proposed and applied to the original images that are later utilized for the training of two modified deep learning models. The deep transfer learning concept is selected for the training of pretrained modified models that are later employed for feature extraction. Features of both deep models are fused using improved canonical correlation analysis that is further optimized using a hybrid algorithm named Whale-Elephant Herding. Through this algorithm, the best features are selected and classified using an extreme learning machine (ELM). Moreover, the modified deep models are utilized for Grad-CAM visualization. The experimental process was conducted on three publicly available datasets and achieved accuracies of 99.1, 98.2, and 96.7%, respectively. Moreover, the ablation study was performed and showed that the proposed accuracy is better than the other methods.

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Explainable Artificial Intelligence for Bias Detection in COVID CT-Scan Classifiers

Cited by 27 publications

References 20 publications

Using deep-learning in fetal ultrasound analysis for diagnosis of cystic hygroma in the first trimester

Using deep-learning in fetal ultrasound analysis for diagnosis of cystic hygroma in the first trimester

Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review

COVID-19 Classification from Chest X-Ray Images: A Framework of Deep Explainable Artificial Intelligence

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