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Bibliography:

The version of denoising-diffuson-pytorch with conditional image generation used in this paper https://github.com/bvale1/denoising-diffusion-pytorch was forked from https://github.com/lucidrains pytorch implementation of Denoising Diffusion Probabilistic Models https://github.com/lucidrains/denoising-diffusion-pytorch @inproceedings{NEURIPS2020_4c5bcfec, author = {Ho, Jonathan and Jain, Ajay and Abbeel, Pieter}, booktitle = {Advances in Neural Information Processing Systems}, editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin}, pages = {6840--6851}, publisher = {Curran Associates, Inc.}, title = {Denoising Diffusion Probabilistic Models}, url = {https://proceedings.neurips.cc/paper/2020/file/4c5bcfec8584af0d967f1ab10179ca4b-Paper.pdf}, volume = {33}, year = {2020} } See https://github.com/lucidrains/denoising-diffusion-pytorch, https://github.com/bvale1/denoising-diffusion-pytorch or denoising-diffusion-pytorch/README.md for the full bibliography of this repository.

The code in the folder 'end_to_end_phantom_QPAT' is forked from https://github.com/BohndiekLab/end_to_end_phantom_QPAT The LICENSE.md is provided. @article{Janek2023IEEE, author = {Janek Gröhl and Thomas R Else and Lina Hacker and Ellie V Bunce and Paul W Sweeney and Sarah E Bohndiek}, journal = {IEEE Transactions on Medical Imaging}, publisher = {IEEE}, title = {Moving beyond simulation: data-driven quantitative photoacoustic imaging using tissue-mimicking phantoms}, year = {2023}, } The correponding experimental dataset @article{grohl2023dataset, title={Dataset for: Moving beyond simulation: data-driven quantitative photoacoustic imaging using tissue-mimicking phantoms}, author={Gr{"o}hl, Janek and Else, Thomas and Hacker, Lina and Bunce, Ellie and Sweeney, Paul and Bohndiek, Sarah}, year={2023} }