Analyzing the Impact of Data Augmentation on the Explainability of Deep Learning-Based Medical Image Classification
Deep learning models are widely used for medical image analysis and require large datasets, while sufficient high-quality medical data for training are scarce. Data augmentation has been used to improve the performance of these models. The lack of transparency of complex deep-learning models raises...
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MDPI AG
2024-12-01
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| author | Xinyu (Freddie) Liu Gizem Karagoz Nirvana Meratnia |
| author_facet | Xinyu (Freddie) Liu Gizem Karagoz Nirvana Meratnia |
| author_sort | Xinyu (Freddie) Liu |
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| description | Deep learning models are widely used for medical image analysis and require large datasets, while sufficient high-quality medical data for training are scarce. Data augmentation has been used to improve the performance of these models. The lack of transparency of complex deep-learning models raises ethical and judicial concerns inducing a lack of trust by both medical experts and patients. In this paper, we focus on evaluating the impact of different data augmentation methods on the explainability of deep learning models used for medical image classification. We investigated the performance of different traditional, mixing-based, and search-based data augmentation techniques with DenseNet121 trained on chest X-ray datasets. We evaluated how the explainability of the model through correctness and coherence can be impacted by these data augmentation techniques. Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) methods were used. Sanity checks and overlapping scores were applied to confirm the correctness and coherence of explainability. The results indicate that both LIME and SHAP passed the sanity check regardless of the type of data augmentation method used. Overall, TrivialAugment performs the best on completeness and coherence. Flipping + cropping performs better on coherence using LIME. Generally, the overlapping scores for SHAP were lower than those for LIME, indicating that LIME has a better performance in terms of coherence. |
| format | Article |
| id | doaj-art-b5a1ea5e2dab4f9e942125bfd3c93a18 |
| institution | OA Journals |
| issn | 2504-4990 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machine Learning and Knowledge Extraction |
| spelling | doaj-art-b5a1ea5e2dab4f9e942125bfd3c93a182025-08-20T02:11:17ZengMDPI AGMachine Learning and Knowledge Extraction2504-49902024-12-0171110.3390/make7010001Analyzing the Impact of Data Augmentation on the Explainability of Deep Learning-Based Medical Image ClassificationXinyu (Freddie) Liu0Gizem Karagoz1Nirvana Meratnia2Department of Mathematics and Computer Science, Eindhoven University of Technology, 5612 AZ Eindhoven, The NetherlandsDepartment of Mathematics and Computer Science, Eindhoven University of Technology, 5612 AZ Eindhoven, The NetherlandsDepartment of Mathematics and Computer Science, Eindhoven University of Technology, 5612 AZ Eindhoven, The NetherlandsDeep learning models are widely used for medical image analysis and require large datasets, while sufficient high-quality medical data for training are scarce. Data augmentation has been used to improve the performance of these models. The lack of transparency of complex deep-learning models raises ethical and judicial concerns inducing a lack of trust by both medical experts and patients. In this paper, we focus on evaluating the impact of different data augmentation methods on the explainability of deep learning models used for medical image classification. We investigated the performance of different traditional, mixing-based, and search-based data augmentation techniques with DenseNet121 trained on chest X-ray datasets. We evaluated how the explainability of the model through correctness and coherence can be impacted by these data augmentation techniques. Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) methods were used. Sanity checks and overlapping scores were applied to confirm the correctness and coherence of explainability. The results indicate that both LIME and SHAP passed the sanity check regardless of the type of data augmentation method used. Overall, TrivialAugment performs the best on completeness and coherence. Flipping + cropping performs better on coherence using LIME. Generally, the overlapping scores for SHAP were lower than those for LIME, indicating that LIME has a better performance in terms of coherence.https://www.mdpi.com/2504-4990/7/1/1explainable AImedical image analysisdata augmentation |
| spellingShingle | Xinyu (Freddie) Liu Gizem Karagoz Nirvana Meratnia Analyzing the Impact of Data Augmentation on the Explainability of Deep Learning-Based Medical Image Classification Machine Learning and Knowledge Extraction explainable AI medical image analysis data augmentation |
| title | Analyzing the Impact of Data Augmentation on the Explainability of Deep Learning-Based Medical Image Classification |
| title_full | Analyzing the Impact of Data Augmentation on the Explainability of Deep Learning-Based Medical Image Classification |
| title_fullStr | Analyzing the Impact of Data Augmentation on the Explainability of Deep Learning-Based Medical Image Classification |
| title_full_unstemmed | Analyzing the Impact of Data Augmentation on the Explainability of Deep Learning-Based Medical Image Classification |
| title_short | Analyzing the Impact of Data Augmentation on the Explainability of Deep Learning-Based Medical Image Classification |
| title_sort | analyzing the impact of data augmentation on the explainability of deep learning based medical image classification |
| topic | explainable AI medical image analysis data augmentation |
| url | https://www.mdpi.com/2504-4990/7/1/1 |
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