Augmenting Chest X-ray Datasets with Non-Expert Annotations
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866909695099273216 |
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| author | Cheplygina, Veronika Damgaard, Cathrine Eriksen, Trine Naja Juodelyte, Dovile Jiménez-Sánchez, Amelia |
| author_facet | Cheplygina, Veronika Damgaard, Cathrine Eriksen, Trine Naja Juodelyte, Dovile Jiménez-Sánchez, Amelia |
| contents | The advancement of machine learning algorithms in medical image analysis requires the expansion of training datasets. A popular and cost-effective approach is automated annotation extraction from free-text medical reports, primarily due to the high costs associated with expert clinicians annotating medical images, such as chest X-rays. However, it has been shown that the resulting datasets are susceptible to biases and shortcuts. Another strategy to increase the size of a dataset is crowdsourcing, a widely adopted practice in general computer vision with some success in medical image analysis. In a similar vein to crowdsourcing, we enhance two publicly available chest X-ray datasets by incorporating non-expert annotations. However, instead of using diagnostic labels, we annotate shortcuts in the form of tubes. We collect 3.5k chest drain annotations for NIH-CXR14, and 1k annotations for four different tube types in PadChest, and create the Non-Expert Annotations of Tubes in X-rays (NEATX) dataset. We train a chest drain detector with the non-expert annotations that generalizes well to expert labels. Moreover, we compare our annotations to those provided by experts and show "moderate" to "almost perfect" agreement. Finally, we present a pathology agreement study to raise awareness about the quality of ground truth annotations. We make our dataset available on Zenodo at https://zenodo.org/records/14944064 and our code available at https://github.com/purrlab/chestxr-label-reliability. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2309_02244 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Augmenting Chest X-ray Datasets with Non-Expert Annotations Cheplygina, Veronika Damgaard, Cathrine Eriksen, Trine Naja Juodelyte, Dovile Jiménez-Sánchez, Amelia Computer Vision and Pattern Recognition The advancement of machine learning algorithms in medical image analysis requires the expansion of training datasets. A popular and cost-effective approach is automated annotation extraction from free-text medical reports, primarily due to the high costs associated with expert clinicians annotating medical images, such as chest X-rays. However, it has been shown that the resulting datasets are susceptible to biases and shortcuts. Another strategy to increase the size of a dataset is crowdsourcing, a widely adopted practice in general computer vision with some success in medical image analysis. In a similar vein to crowdsourcing, we enhance two publicly available chest X-ray datasets by incorporating non-expert annotations. However, instead of using diagnostic labels, we annotate shortcuts in the form of tubes. We collect 3.5k chest drain annotations for NIH-CXR14, and 1k annotations for four different tube types in PadChest, and create the Non-Expert Annotations of Tubes in X-rays (NEATX) dataset. We train a chest drain detector with the non-expert annotations that generalizes well to expert labels. Moreover, we compare our annotations to those provided by experts and show "moderate" to "almost perfect" agreement. Finally, we present a pathology agreement study to raise awareness about the quality of ground truth annotations. We make our dataset available on Zenodo at https://zenodo.org/records/14944064 and our code available at https://github.com/purrlab/chestxr-label-reliability. |
| title | Augmenting Chest X-ray Datasets with Non-Expert Annotations |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2309.02244 |