DOI: https://doi.org/10.21203/rs.3.rs-776478/v1
Background: Machine learning, especially deep learning, is becoming more and more relevant in research and development in the medical domain. For all of the supervised deep learning applications, data is the most critical factor in securing successful implementation and sustaining the progress of the machine learning model. Especially gastroenterological data, which often involves endoscopic videos, are cumbersome to annotate. Domain experts are needed to interpret and annotate the videos. To support those domain experts, we generated a framework. With this framework, instead of annotating every frame in the video sequence, experts are just performing key annotations at the beginning and the end of sequences with pathologies, e.g. visible polyps. Subsequently, non-expert annotators supported by machine learning add the missing annotations for the frames in-between.
Results: Using this framework we were able to reduce work load of domain experts on average by a factor of 20. This is primarily due to the structure of the framework, which is designed to minimize the workload of the domain expert. Pairing this framework with a state-of-the-art semi-automated pre-annotation model enhances the annotation speed further. Through a study with 10 participants we show that semi-automated annotation using our tool doubles the annotation speed of non-expert annotators compared to a well-known state-of-the-art annotation tool.
Conclusion: In summary, we introduce a framework for fast expert annotation for gastroenterologists, which reduces the workload of the domain expert considerably while maintaining a very high annotation quality. The framework incorporates a semi-automated annotation system utilizing trained object detection models. The software and framework are open-source.