Rucha Deshpande,1 Mark A. Anastasio,2 Frank J. Brooks2,3
1Washington Univ. in St. Louis (United States) 2Univ. of Illinois (United States) 3Beckman Institute for Advanced Science and Technology (United States)
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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.
Rucha Deshpande,Mark A. Anastasio, andFrank 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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Rucha Deshpande, Mark A. Anastasio, 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