TITLE: Structured dataset documentation: a datasheet for CheXpert 

ABSTRACT: Billions of X-ray images are taken worldwide each year. Machine learning, and
deep learning in particular, has shown potential to help radiologists triage
and diagnose images. However, deep learning requires large datasets with
reliable labels. The CheXpert dataset was created with the participation of
board-certified radiologists, resulting in the strong ground truth needed to
train deep learning networks. Following the structured format of Datasheets for
Datasets, this paper expands on the original CheXpert paper and other sources
to show the critical role played by radiologists in the creation of reliable
labels and to describe the different aspects of the dataset composition in
detail. Such structured documentation intends to increase the awareness in the
machine learning and medical communities of the strengths, applications, and
evolution of CheXpert, thereby advancing the field of medical image analysis.
Another objective of this paper is to put forward this dataset datasheet as an
example to the community of how to create detailed and structured descriptions
of datasets. We believe that clearly documenting the creation process, the
contents, and applications of datasets accelerates the creation of useful and
reliable models.
Billions of X-ray images are taken worldwide each year. Machine learning, and
deep learning in particular, has shown potential to help radiologists triage
and diagnose images. However, deep learning requires large datasets with
reliable labels. The CheXpert dataset was created with the participation of
board-certified radiologists, resulting in the strong ground truth needed to
train deep learning networks. Following the structured format of Datasheets for
Datasets, this paper expands on the original CheXpert paper and other sources
to show the critical role played by radiologists in the creation of reliable
labels and to describe the different aspects of the dataset composition in
detail. Such structured documentation intends to increase the awareness in the
machine learning and medical communities of the strengths, applications, and
evolution of CheXpert, thereby advancing the field of medical image analysis.
Another objective of this paper is to put forward this dataset datasheet as an
example to the community of how to create detailed and structured descriptions
of datasets. We believe that clearly documenting the creation process, the
contents, and applications of datasets accelerates the creation of useful and
reliable models.
Structured dataset documentation: a datasheet for CheXpert
http://arxiv.org/abs/2105.03020v1