MIDRC-MetricTree.

An interactive Decision Support Tool for Evaluating Machine Learning Algorithm Performance in Medical Image Analysis

Brought to you by MIDRC Technology and Development Projects 3c and 3d.

Karen Drukker, Berkman Sahiner, Natalie Baughan, Maryellen Giger, Tingting Hu, Grace Kim, Michael McNitt-Gray, Kyle Myers, Heather Whitney

Last updated April 22, 2024

Please cite: Drukker, Karen, Berkman Sahiner, Tingting Hu, Grace Hyun Kim, Heather M. Whitney, Natalie Baughan, Kyle J. Myers, Maryellen L. Giger, Michael McNitt-Gray, MIDRC-MetricTree: a decision tree-based tool for recommending performance metrics in artificial intelligence-assisted medical image analysis, J. Med. Imag. 11(2), 024504 (2024), doi: 10.1117/1.JMI.11.2.024504.

Note that our decision tree focuses on the recommendation of performance metrics for different medical imaging AI tasks. For other aspects of AI model development, training, and testing, please consult a recent AAPM task group report for which multiple co-authors are involved in MIDRC:

AAPM task group report 273: Recommendations on best practices for AI and machine learning for computer-aided diagnosis in medical imaging

Lubomir Hadjiiski,  Kenny Cha,  Heang-Ping Chan,  Karen Drukker,  Lia Morra,  Janne J. Näppi,  Berkman Sahiner,  Hiroyuki Yoshida,  Quan Chen,  Thomas M. Deserno,  Hayit Greenspan,  Henkjan Huisman,  Zhimin Huo,  Richard Mazurchuk,  Nicholas Petrick,  Daniele Regge,  Ravi Samala,  Ronald M. Summers,  Kenji Suzuki,  Georgia Tourassi,  Daniel Vergara,  Samuel G. Armato III,, Med Phys. 2023; 50: e1– e24. https://doi.org/10.1002/mp.16188