Miles Hutson
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Iterative quality control strategies for expert medical image labeling
Sonia Phene
Abigail Huang
Rebecca Ackermann
Olga Kanzheleva
Caitlin Taggart
Proceedings of the AAAI Conference on Human Computation and Crowdsourcing (2021)
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Data quality is a key concern for artificial intelligence (AI) efforts that rely upon crowdsourced data collection. In the domain of medicine in particular, labeled data must meet higher quality standards, or the resulting AI may lead to patient harm, and/or perpetuate biases. What are the challenges involved in expert medical labeling? What processes do such teams employ? In this study, we interviewed members of teams developing AI for medical imaging across 4 subdomains (ophthalmology, radiology, pathology, and dermatology). We identify a set of common practices for ensuring data quality. We describe one instance of low-quality labeling caught by post-launch monitoring. However, the more common pattern is to involve experts in an iterative process of defining, testing, and iterating tasks and instructions. Teams invest in these upstream efforts in order to mitigate downstream quality issues during large-scale labeling.
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Preview abstract
Crowdsourcing has enabled the collection, aggregation and refinement of human knowledge and judgment, i.e. ground truth, for problem domains with data of increasing complexity and scale.
This scale of ground truth data generation, especially towards the development of machine learning based medical applications that require large volumes of consistent diagnoses, poses significant and unique challenges to quality control.
Poor quality control in crowdsourced labeling of medical data can result in undesired effects on patients' health.
In this paper, we study medicine-specific quality control problems, including the diversity of grader expertise and diagnosis guidelines' ambiguity in novel datasets of three eye diseases.
We present analytical findings on physicians' work patterns, evaluate existing quality control methods that rely on task completion time to circumvent the scarcity and cost problem of generating ground truth medical data, and share our experiences with a real-world system that collects medical labels at scale.
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