Colorectal Polyp Detection in Real-world Scenario: Design and Experiment Study

Sun, Xiuhong; Wang, Dechun; Zhang, Chenxi; Zhang, Pengfei; Xiong, Zinan; Cao, Yu; Benyuan, Liu; Liu, Xiaowei; Chen, Shuijiao

doi:10.1109/ictai50040.2020.00113

Cited by 11 publications

(1 citation statement)

References 24 publications

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“…The GIANA dataset consists of 18 annotated polyp sequences. It is a standard dataset that has been used before for model benchmarking in different publications [34,35,36]. Therefore, we can reliably use it for evaluating the quality of our results.…”

Section: Data Acquisition and Experimental Set Upmentioning

confidence: 99%

Fast Machine Learning Annotation in the Medical Domain: A Semi-Automated Video Annotation Tool for Gastroenterologists

Krenzer

Makowski

Hekalo

et al. 2021

Preprint

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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.

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Section: Data Acquisition and Experimental Set Upmentioning

confidence: 99%