MACAW 3D: A masked causal normalizing flow method for counterfactual 3D brain image generation

Erik Y. Ohara; Finn Vamosi; Harsh Patil; Vibujithan Vigneshwaran; Matthias Wilms; Nils D. Forkert

doi:10.1117/12.3006482

2 April 2024 MACAW 3D: A masked causal normalizing flow method for counterfactual 3D brain image generation

Erik Y. Ohara, Finn Vamosi, Harsh Patil, Vibujithan Vigneshwaran, Matthias Wilms, Nils D. Forkert

Proceedings Volume 12931, Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and Applications; 129310K (2024) https://doi.org/10.1117/12.3006482
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

Deep learning techniques for medical image analysis have reached comparable performance to medical experts, but the lack of reliable explainability leads to limited adoption in clinical routine. Explainable AI has emerged to address this issue, with causal generative techniques standing out by incorporating a causal perspective into deep learning models. However, their use cases have been limited to 2D images and tabulated data. To overcome this, we propose a novel method to expand a causal generative framework to handle volumetric 3D images, which was validated through analyzing the effect of brain aging using 40196 MRI datasets from the UK Biobank study. Our proposed technique paves the way for future 3D causal generative models in medical image analysis.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Erik Y. Ohara, Finn Vamosi, Harsh Patil, Vibujithan Vigneshwaran, Matthias Wilms, and Nils D. Forkert "MACAW 3D: A masked causal normalizing flow method for counterfactual 3D brain image generation", Proc. SPIE 12931, Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and Applications, 129310K (2 April 2024); https://doi.org/10.1117/12.3006482

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