Exploring a method to evaluate image-conditioned deep generative models for their capacity to reproduce domain-relevant spatial context

Rucha Deshpande; Mark A. Anastasio; Frank J. Brooks

doi:10.1117/12.3006469

30 March 2024 Exploring a method to evaluate image-conditioned deep generative models for their capacity to reproduce domain-relevant spatial context

Rucha Deshpande, Mark A. Anastasio, Frank J. Brooks

Author Affiliations +

Proceedings Volume 12929, Medical Imaging 2024: Image Perception, Observer Performance, and Technology Assessment; 1292912 (2024) https://doi.org/10.1117/12.3006469
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

In domains such as biomedical imaging, the evaluation of deep generative models (DGMs) for image-to-image translation tasks is additionally challenged by the need for substantial domain expertise, even for visual evaluation. To partially circumvent this problem, we propose a data-driven, human interpretable method to evaluate image-conditioned DGMs for the reproducibility of domain-relevant spatial context before the DGMs are considered for diagnostic tasks and real-world deployment.

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Rucha Deshpande, Mark A. Anastasio, and Frank J. Brooks "Exploring a method to evaluate image-conditioned deep generative models for their capacity to reproduce domain-relevant spatial context", Proc. SPIE 12929, Medical Imaging 2024: Image Perception, Observer Performance, and Technology Assessment, 1292912 (30 March 2024); https://doi.org/10.1117/12.3006469

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