Augmenting Chest X-ray Datasets with Non-Expert Annotations

• URL: http://arxiv.org/abs/2309.02244v1
• Date: Tue, 5 Sep 2023 13:52:43 GMT
• Title: Augmenting Chest X-ray Datasets with Non-Expert Annotations
• Authors: Cathrine Damgaard, Trine Naja Eriksen, Dovile Juodelyte, Veronika Cheplygina, Amelia Jim\'enez-S\'anchez
• Abstract summary: A popular and cost-effective approach is automated annotation extraction from free-text medical reports. We enhance two publicly available chest X-ray datasets by incorporating non-expert annotations. We train a chest drain detector with the non-expert annotations that generalizes well to expert labels.
• Score: 1.9991771189143435
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 chest X-ray images. 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 CXR14, and 1k annotations for 4 different tube types in PadChest. 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 ground truth annotations. We make our annotations and code available.

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