DRAGen: Decision Region Analysis for Generalizability

DRAGen is an AI/ML model agnostic approach to assess generalizability.

Overview of the decision region generation and analysis process

This repository contains the implementation for the methodology in the paper "Decision region analysis for generalizability of artificial intelligence models: estimating model generalizability in the case of cross-reactivity and population shift". The Open Access paper is available at this link.

Additional References

• Alexis Burgon, Berkman Sahiner, Nicholas Petrick, Gene Pennello, Kenny H. Cha, Ravi K. Samala, "Decision region analysis for generalizability of artificial intelligence models: estimating model generalizability in the case of cross-reactivity and population shift," J. Med. Imag. 11(1) 014501 (25 January 2024) https://doi.org/10.1117/1.JMI.11.1.014501
• Alexis Burgon, Nicholas Petrick, Berkman Sahiner, Gene Pennello, Ravi K. Samala, "Predicting AI model behavior on unrepresented subgroups: A test-time approach to increase variability in a finite test set", 2023 FDA Science Forum. (link)
• Alexis Burgon, Nicholas Petrick, Berkman Sahiner, Gene Pennello, and Ravi K. Samala "Decision region analysis to deconstruct the subgroup influence on AI/ML predictions", Proc. SPIE 12465, Medical Imaging 2023: Computer-Aided Diagnosis, 124651H (7 April 2023); https://doi.org/10.1117/12.2653963

Introduction

Understanding an artificial intelligence (AI) model's ability to generalize to its target population is critical to ensure the safe and effective use of AI in medical devices. Traditional generalizability assessment relies on the availability of large, diverse data sets, which are difficult to obtain for medical imaging. We present an approach for enhanced generalizability assessment by examining the decision space beyond the available test set.

A vicinal distribution of virtual images is created by linearly interpolating between a sample "triplet" of three images. The composition of the region of the decision space is then approximated from the model inference on the virtual images. Aggregating the decision region compositions from many triplets provides insight into the overall decision region composition.

Getting Started

For detailed information, view the DRAGen documentation.

An interactive example of how to use this repository can be found in the example notebook.

Example Output

Example decision region composition plot. The decision region compositions are aggregated based on the class of the sample triplet.

System Requirements

python 3.10.6 Ubuntu 22.04.2 LTS Python package requirements can be found in requirements.txt.

Data

The data included in the examples folder were accessed through TCIA: - Saltz, J., Saltz, M., Prasanna, P., Moffitt, R., Hajagos, J., Bremer, E., Balsamo, J., & Kurc, T. (2021). Stony Brook University COVID-19 Positive Cases [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/TCIA.BBAG-2923 - Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. DOI: 10.1007/s10278-013-9622-7