README.md
In DIDSR/HTT: Data, scripts, and functions of the High-Throughput Truthing project

HTT: R data package

Data, scripts, and functions of the High-Throughput Truthing project (HTT project). The "inst" directory will be used to archive scripts that reproduce the analyses done for different presentations and publications.

Project hub space: https://ncihub.org/groups/eedapstudies

To install this package from the R command line: install_github('DIDSR/HTT')

Data and Code Repository

https://github.com/DIDSR/HTT

Manual documenting the data and functions in the R package:

https://github.com/DIDSR/HTT/tree/main/inst/manual

Webinar tour of the data: Link to video

Manuscript describing the project:

Dudgeon et al. (2020), "A Pathologist-Annotated Dataset for Validating Artificial Intelligence: A Project Description and Pilot Study," Journal of Pathology Informatics, 12, p. 45. https://www.doi.org/10.4103/jpi.jpi_83_20

Manuscript describing expert panel based on pilot study:

Garcia et al. (2022), “Development of Training Materials for Pathologists to Provide Machine Learning Validation Data of Tumor-Infiltrating Lymphocytes in Breast Cancer,” Cancers, 14, p. 2467, https://www.doi.org/10.3390/cancers14102467.

Manuscript describing pilot study results:

Elfer et al. (2022), “Pilot study to evaluate tools to collect pathologist annotations for validating machine learning algorithms,” J. Med. Imag., 9, p. 047501, https://www.doi.org/10.1117/1.JMI.9.4.047501.

Manuscript about Multi-reader Multi-case Analysis of Limits of Agreement

Wen and Gallas (2022), “Three-Way Mixed Effect ANOVA to Estimate MRMC Limits of Agreement,” Stat Biopharm Res, p. 1–10, https://www.doi.org/10.1080/19466315.2022.2063169.