Exploring the Efficacy of Base Data Augmentation Methods in Deep Learning-Based Radiograph Classification of Knee Joint Osteoarthritis Article Swipe
Fabi Prezja
,
Leevi Annala
,
Sampsa Kiiskinen
,
Timo Ojala
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2311.06118
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2311.06118
Diagnosing knee joint osteoarthritis (KOA), a major cause of disability worldwide, is challenging due to subtle radiographic indicators and the varied progression of the disease. Using deep learning for KOA diagnosis requires broad, comprehensive datasets. However, obtaining these datasets poses significant challenges due to patient privacy concerns and data collection restrictions. Additive data augmentation, which enhances data variability, emerges as a promising solution. Yet, it's unclear which augmentation techniques are most effective for KOA. This study explored various data augmentation methods, including adversarial augmentations, and their impact on KOA classification model performance. While some techniques improved performance, others commonly used underperformed. We identified potential confounding regions within the images using adversarial augmentation. This was evidenced by our models' ability to classify KL0 and KL4 grades accurately, with the knee joint omitted. This observation suggested a model bias, which might leverage unrelated features for classification currently present in radiographs. Interestingly, removing the knee joint also led to an unexpected improvement in KL1 classification accuracy. To better visualize these paradoxical effects, we employed Grad-CAM, highlighting the associated regions. Our study underscores the need for careful technique selection for improved model performance and identifying and managing potential confounding regions in radiographic KOA deep learning.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2311.06118
- https://arxiv.org/pdf/2311.06118
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388650484
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388650484Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2311.06118Digital Object Identifier
- Title
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Exploring the Efficacy of Base Data Augmentation Methods in Deep Learning-Based Radiograph Classification of Knee Joint OsteoarthritisWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-11-10Full publication date if available
- Authors
-
Fabi Prezja, Leevi Annala, Sampsa Kiiskinen, Timo OjalaList of authors in order
- Landing page
-
https://arxiv.org/abs/2311.06118Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2311.06118Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2311.06118Direct OA link when available
- Concepts
-
Osteoarthritis, Confounding, Artificial intelligence, Deep learning, Leverage (statistics), Machine learning, Computer science, Radiography, Medicine, Surgery, Alternative medicine, PathologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.radiographs. | 147 |
| abstract_inverted_index.variability, | 57 |
| abstract_inverted_index.augmentation, | 53 |
| abstract_inverted_index.augmentation. | 111 |
| abstract_inverted_index.comprehensive | 33 |
| abstract_inverted_index.restrictions. | 50 |
| abstract_inverted_index.Interestingly, | 148 |
| abstract_inverted_index.augmentations, | 83 |
| abstract_inverted_index.classification | 89, 143, 161 |
| abstract_inverted_index.osteoarthritis | 3 |
| abstract_inverted_index.underperformed. | 100 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.4699999988079071 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.3635512 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |