Annotating Ambiguous Images: General Annotation Strategy for High-Quality Data with Real-World Biomedical Validation
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866914774642589696 |
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| author | Schmarje, Lars Grossmann, Vasco Zelenka, Claudius Brünger, Johannes Koch, Reinhard |
| author_facet | Schmarje, Lars Grossmann, Vasco Zelenka, Claudius Brünger, Johannes Koch, Reinhard |
| contents | In the field of image classification, existing methods often struggle with biased or ambiguous data, a prevalent issue in real-world scenarios. Current strategies, including semi-supervised learning and class blending, offer partial solutions but lack a definitive resolution. Addressing this gap, our paper introduces a novel strategy for generating high-quality labels in challenging datasets. Central to our approach is a clearly designed flowchart, based on a broad literature review, which enables the creation of reliable labels. We validate our methodology through a rigorous real-world test case in the biomedical field, specifically in deducing height reduction from vertebral imaging. Our empirical study, leveraging over 250,000 annotations, demonstrates the effectiveness of our strategies decisions compared to their alternatives. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_12189 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Annotating Ambiguous Images: General Annotation Strategy for High-Quality Data with Real-World Biomedical Validation Schmarje, Lars Grossmann, Vasco Zelenka, Claudius Brünger, Johannes Koch, Reinhard Computer Vision and Pattern Recognition In the field of image classification, existing methods often struggle with biased or ambiguous data, a prevalent issue in real-world scenarios. Current strategies, including semi-supervised learning and class blending, offer partial solutions but lack a definitive resolution. Addressing this gap, our paper introduces a novel strategy for generating high-quality labels in challenging datasets. Central to our approach is a clearly designed flowchart, based on a broad literature review, which enables the creation of reliable labels. We validate our methodology through a rigorous real-world test case in the biomedical field, specifically in deducing height reduction from vertebral imaging. Our empirical study, leveraging over 250,000 annotations, demonstrates the effectiveness of our strategies decisions compared to their alternatives. |
| title | Annotating Ambiguous Images: General Annotation Strategy for High-Quality Data with Real-World Biomedical Validation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2306.12189 |