Enhancing Annotation Efficiency with Machine Learning: Automated Partitioning of a Lung Ultrasound Dataset by View.

ABSTRACT:

Background

Annotating large medical imaging datasets is an arduous and expensive task, especially when the datasets in question are not organized according to deep learning goals. Here, we propose a method that exploits the hierarchical organization of annotating tasks to optimize efficiency.

Methods

We trained a machine learning model to accurately distinguish between one of two classes of lung ultrasound (LUS) views using 2908 clips from a larger dataset. Partitioning the remaining dataset by view would reduce downstream labelling efforts by enabling annotators to focus on annotating pathological features specific to each view.

Results

In a sample view-specific annotation task, we found that automatically partitioning a 780-clip dataset by view saved 42 min of manua

SUBMITTER: VanBerlo B 

PROVIDER: S-EPMC9601089 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature