Evaluating the capacity of a diffusion generative model to reproduce spatial context relevant to diagnostic imaging

Rucha Deshpande; Muzaffer Ozbey; Hua Li; Mark A. Anastasio; Frank J. Brooks

doi:10.1117/12.3006845

30 March 2024 Evaluating the capacity of a diffusion generative model to reproduce spatial context relevant to diagnostic imaging

Rucha Deshpande, Muzaffer Ozbey, Hua Li, Mark A. Anastasio, Frank J. Brooks

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Proceedings Volume 12929, Medical Imaging 2024: Image Perception, Observer Performance, and Technology Assessment; 129290I (2024) https://doi.org/10.1117/12.3006845
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

The rapid evolution of deep generative models (DGMs) has highlighted their great potential in medical imaging research. Recently, it has been claimed that a diffusion generative model: denoising diffusion probabilistic model (DDPM), performs better at image synthesis than the previously popular DGMs: generative adversarial networks (GANs). However, this claim is based on evaluations employing measures intended for natural images, and thus, does not resolve questions about their relevance to medical imaging tasks. To partially address this problem, we performed a series of assessments to evaluate the ability of a DDPM to reproduce diagnostically relevant spatial context. Our findings show that in all our studies, although context was generally well replicated in DDPM-generated ensembles, it was never perfectly reproduced in the entire ensemble.

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Rucha Deshpande, Muzaffer Ozbey, Hua Li, Mark A. Anastasio, and Frank J. Brooks "Evaluating the capacity of a diffusion generative model to reproduce spatial context relevant to diagnostic imaging", Proc. SPIE 12929, Medical Imaging 2024: Image Perception, Observer Performance, and Technology Assessment, 129290I (30 March 2024); https://doi.org/10.1117/12.3006845

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KEYWORDS

Diffusion

Diagnostics

Reproducibility

Anatomy

Image quality

Performance modeling

Gallium nitride

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