Dataset Information

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 manual annotation time and resulted in 55±6 additional relevant labels per hour.

Conclusions

Automatic partitioning of a LUS dataset by view significantly increases annotator efficiency, resulting in higher throughput relevant to the annotating task at hand. The strategy described in this work can be applied to other hierarchical annotation schemes.

SUBMITTER: VanBerlo B

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

REPOSITORIES: biostudies-literature

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Publications

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

VanBerlo Bennett B Smith Delaney D Tschirhart Jared J VanBerlo Blake B Wu Derek D Ford Alex A McCauley Joseph J Wu Benjamin B Chaudhary Rushil R Dave Chintan C Ho Jordan J Deglint Jason J Li Brian B Arntfield Robert R

Diagnostics (Basel, Switzerland) 20220928 10

<h4>Background</h4>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.<h4>Methods</h4>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 remain ...[more]

PMID: 36292042

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Dataset Information

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

Background

Methods

Results

Conclusions

Publications

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

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