Adjoint operators enable fast and amortized machine learning based Bayesian uncertainty quantification

Rafael Orozco; Ali Siahkoohi; Gabrio Rizzuti; Tristan van Leeuwen; Felix J. Herrmann

doi:10.1117/12.2651691

3 April 2023 Adjoint operators enable fast and amortized machine learning based Bayesian uncertainty quantification

Rafael Orozco, Ali Siahkoohi, Gabrio Rizzuti, Tristan van Leeuwen, Felix J. Herrmann

Proceedings Volume 12464, Medical Imaging 2023: Image Processing; 124641L (2023) https://doi.org/10.1117/12.2651691
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

In the context of machine learning for uncertainty quantification (UQ) of inverse problems: we propose to first transform input observations using the adjoint. We demonstrate with two imaging examples (photoacoustic imaging and CT) that firstly: the adjoint partially undoes the physics of the problem resulting in faster convergence of the learning phase and secondly: the final algorithm is now robust to changes in observations such as changing transducer subsampling in photoacoustic imaging and angles in CT. Our adjoint-based method gives point estimates faster than traditional baselines and with higher SSIM metrics, while also providing validated UQ.

Citation Download Citation

Rafael Orozco, Ali Siahkoohi, Gabrio Rizzuti, Tristan van Leeuwen, and Felix J. Herrmann "Adjoint operators enable fast and amortized machine learning based Bayesian uncertainty quantification", Proc. SPIE 12464, Medical Imaging 2023: Image Processing, 124641L (3 April 2023); https://doi.org/10.1117/12.2651691

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