Decision region analysis to deconstruct the subgroup influence on AI/ML predictions

Alexis Burgon; Nicholas Petrick; Berkman Sahiner; Gene Pennello; Ravi K. Samala

doi:10.1117/12.2653963

7 April 2023 Decision region analysis to deconstruct the subgroup influence on AI/ML predictions

Alexis Burgon, Nicholas Petrick, Berkman Sahiner, Gene Pennello, Ravi K. Samala

Proceedings Volume 12465, Medical Imaging 2023: Computer-Aided Diagnosis; 124651H (2023) https://doi.org/10.1117/12.2653963
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

Assessing the generalizability of deep learning algorithms based on the size and diversity of the training data is not trivial. This study uses the mapping of samples in the image data space to the decision regions in the prediction space to understand how different subgroups in the data impact the neural network learning process and affect model generalizability. Using vicinal distribution-based linear interpolation, a plane of the decision region space spanned by the random ‘triplet’ of three images can be constructed. Analyzing these decision regions for many random triplets can provide insight into the relationships between distinct subgroups. In this study, a contrastive self-supervised approach is used to develop a ‘base’ classification model trained on a large chest x-ray (CXR) dataset. The base model is fine-tuned on COVID-19 CXR data to predict image acquisition technology (computed radiography (CR) or digital radiography (DX) and patient sex (male (M) or female (F)). Decision region analysis shows that the model’s image acquisition technology decision space is dominated by CR, regardless of the acquisition technology for the base images. Similarly, the Female class dominates the decision space. This study shows that decision region analysis has the potential to provide insights into subgroup diversity, sources of imbalances in the data, and model generalizability.

Conference Presentation

Citation Download Citation

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

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