Can non-specialists provide high quality gold standard labels in challenging modalities?

TL;DR

This paper investigates whether minimally trained novices can produce high-quality annotations for complex medical images, potentially reducing costs and addressing data scarcity in medical deep learning applications.

Contribution

It challenges the assumption that only experts can provide useful annotations, demonstrating the viability of novice annotations for training effective deep learning models in medical imaging.

Findings

Novice annotators can produce useful labels with minimal training.

Using novice annotations can reduce labeling costs and time.

Deep learning models trained on novice labels perform comparably to those trained on expert labels.

Abstract

Probably yes. -- Supervised Deep Learning dominates performance scores for many computer vision tasks and defines the state-of-the-art. However, medical image analysis lags behind natural image applications. One of the many reasons is the lack of well annotated medical image data available to researchers. One of the first things researchers are told is that we require significant expertise to reliably and accurately interpret and label such data. We see significant inter- and intra-observer variability between expert annotations of medical images. Still, it is a widely held assumption that novice annotators are unable to provide useful annotations for use by clinical Deep Learning models. In this work we challenge this assumption and examine the implications of using a minimally trained novice labelling workforce to acquire annotations for a complex medical image dataset. We study the time and cost implications of using novice annotators, the raw performance of novice annotators compared to gold-standard expert annotators, and the downstream effects on a trained Deep Learning segmentation model's performance for detecting a specific congenital heart disease (hypoplastic left heart syndrome) in fetal ultrasound imaging.