\begin{thebibliography}{52}
\providecommand{\natexlab}[1]{#1}
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  \providecommand{\doi}[1]{doi: #1}\else
  \providecommand{\doi}{doi: \begingroup \urlstyle{rm}\Url}\fi

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Miko{\l}aj Bi\'{n}kowski, Dougal~J. Sutherland, Michael Arbel, and Arthur Gretton.
\newblock Demystifying {MMD} {GAN}s.
\newblock In \emph{International Conference on Learning Representations}, 2018.

\bibitem[Buzuti and Thomaz(2023)]{buzuti2023frechet}
Lucas~F. Buzuti and Carlos~E. Thomaz.
\newblock Fr{\'e}chet {A}uto{E}ncoder distance: a new approach for evaluation of generative adversarial networks.
\newblock \emph{Computer Vision and Image Understanding}, 235:\penalty0 103768, 2023.

\bibitem[Chen et~al.(2024)Chen, Mei, Li, Lu, Yu, Wei, Luo, Xie, Adeli, Wang, Lungren, Zhang, Xing, Lu, Yuille, and Zhou]{chen2024transunet}
Jieneng Chen, Jieru Mei, Xianhang Li, Yongyi Lu, Qihang Yu, Qingyue Wei, Xiangde Luo, Yutong Xie, Ehsan Adeli, Yan Wang, Matthew~P. Lungren, Shaoting Zhang, Lei Xing, Le~Lu, Alan Yuille, and Yuyin Zhou.
\newblock {TransUNet}: Rethinking the {U-Net} architecture design for medical image segmentation through the lens of transformers.
\newblock \emph{Medical Image Analysis}, 97:\penalty0 103280, 2024.

\bibitem[Chong and Forsyth(2020)]{chong2020effectively}
Min~Jin Chong and David Forsyth.
\newblock Effectively unbiased {FID} and inception score and where to find them.
\newblock In \emph{Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages 6070--6079, 2020.

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Coen De~Vente, Koenraad~A. Vermeer, Nicolas Jaccard, He~Wang, Hongyi Sun, Firas Khader, Daniel Truhn, Temirgali Aimyshev, Yerkebulan Zhanibekuly, Tien-Dung Le, Adrian Galdran, Miguel {\'A}ngel~Gonz{\'a}lez Ballester, Gustavo Carneiro, R.~G. Devika, Hrishikesh~Panikkasseril Sethumadhavan, Densen Puthussery, Hong Liu, Zekang Yang, Satoshi Kondo, Satoshi Kasai, Edward Wang, Ashritha Durvasula, J{\'o}nathan Heras, Miguel~{\'A}ngel Zapata, Teresa Ara{\'u}jo, Guilherme Aresta, Hrvoje Bogunovi{\'c}, Mustafa Arikan, Yeong~Chan Lee, Hyun~Bin Cho, Yoon~Ho Choi, Abdul Qayyum, Imran Razzak, Bram van Ginneken, Hans~G. Lemij, and Clara~I. S{\'a}nchez.
\newblock {AIROGS}: Artificial intelligence for robust glaucoma screening challenge.
\newblock \emph{IEEE Transactions on Medical Imaging}, 43\penalty0 (1):\penalty0 542--557, 2023.

\bibitem[Deng et~al.(2009)Deng, Dong, Socher, Li, Li, and Fei-Fei]{deng2009imagenet}
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li~Fei-Fei.
\newblock {ImageNet}: A large-scale hierarchical image database.
\newblock In \emph{Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, pages 248--255, 2009.

\bibitem[Dowson and Landau(1982)]{dowson1982frechet}
D.~C. Dowson and B.~V. Landau.
\newblock The {Fr{\'e}chet} distance between multivariate normal distributions.
\newblock \emph{Journal of Multivariate Analysis}, 12\penalty0 (3):\penalty0 450--455, 1982.

\bibitem[Eschweiler et~al.(2024)Eschweiler, Yilmaz, Baumann, Laube, Roy, Jose, Br{\"u}ckner, and Stegmaier]{eschweiler2024denoising}
Dennis Eschweiler, R{\"u}veyda Yilmaz, Matisse Baumann, Ina Laube, Rijo Roy, Abin Jose, Daniel Br{\"u}ckner, and Johannes Stegmaier.
\newblock Denoising diffusion probabilistic models for generation of realistic fully-annotated microscopy image datasets.
\newblock \emph{PLOS Computational Biology}, 20\penalty0 (2):\penalty0 e1011890, 2024.

\bibitem[Fang et~al.(2022)Fang, Li, Fu, Wu, Zhang, and Xu]{fang2022dataset}
Huihui Fang, Fei Li, Huazhu Fu, Junde Wu, Xiulan Zhang, and Yanwu Xu.
\newblock Dataset and evaluation algorithm design for {GOALS} challenge.
\newblock In \emph{International Workshop on Ophthalmic Medical Image Analysis}, pages 135--142, 2022.

\bibitem[Fr{\'e}chet(1957)]{frechet1957distance}
Maurice Fr{\'e}chet.
\newblock Sur la distance de deux lois de probabilit{\'e}.
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\bibitem[Goodfellow et~al.(2014)Goodfellow, Pouget-Abadie, Mirza, Xu, Warde-Farley, Ozair, Courville, and Bengio]{goodfellow2014generative}
Ian~J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio.
\newblock Generative adversarial nets.
\newblock \emph{Advances in Neural Information Processing Systems}, 27:\penalty0 2672--2680, 2014.

\bibitem[Gretton et~al.(2006)Gretton, Borgwardt, Rasch, Sch{\"o}lkopf, and Smola]{gretton2006kernel}
Arthur Gretton, Karsten Borgwardt, Malte Rasch, Bernhard Sch{\"o}lkopf, and Alex Smola.
\newblock A kernel method for the two-sample-problem.
\newblock \emph{Advances in Neural Information Processing Systems}, 19:\penalty0 513--520, 2006.

\bibitem[Han et~al.(2020)Han, Nebelung, Haarburger, Horst, Reinartz, Merhof, Kiessling, Schulz, and Truhn]{han2020breaking}
Tianyu Han, Sven Nebelung, Christoph Haarburger, Nicolas Horst, Sebastian Reinartz, Dorit Merhof, Fabian Kiessling, Volkmar Schulz, and Daniel Truhn.
\newblock Breaking medical data sharing boundaries by using synthesized radiographs.
\newblock \emph{Science Advances}, 6\penalty0 (49):\penalty0 eabb7973, 2020.

\bibitem[He et~al.(2016)He, Zhang, Ren, and Sun]{he2016deep}
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun.
\newblock Deep residual learning for image recognition.
\newblock In \emph{Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages 770--778, 2016.

\bibitem[He et~al.(2022)He, Chen, Xie, Li, Doll{\'a}r, and Girshick]{he2022masked}
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Doll{\'a}r, and Ross Girshick.
\newblock Masked autoencoders are scalable vision learners.
\newblock In \emph{Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages 16000--16009, 2022.

\bibitem[Heusel et~al.(2017)Heusel, Ramsauer, Unterthiner, Nessler, and Hochreiter]{heusel2017gans}
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter.
\newblock {GAN}s trained by a two time-scale update rule converge to a local {N}ash equilibrium.
\newblock \emph{Advances in Neural Information Processing Systems}, 30:\penalty0 6626--6637, 2017.

\bibitem[Ho et~al.(2020)Ho, Jain, and Abbeel]{ho2020denoising}
Jonathan Ho, Ajay Jain, and Pieter Abbeel.
\newblock Denoising diffusion probabilistic models.
\newblock \emph{Advances in Neural Information Processing Systems}, 33:\penalty0 6840--6851, 2020.

\bibitem[Hou(2023)]{hou2023high}
Benjamin Hou.
\newblock High-fidelity diabetic retina fundus image synthesis from freestyle lesion maps.
\newblock \emph{Biomedical Optics Express}, 14\penalty0 (2):\penalty0 533--549, 2023.
\newblock \doi{10.1364/BOE.477906}.

\bibitem[Huang et~al.(2024)Huang, Ma, Zhang, Zhang, Yuan, Fu, and Chen]{huang2024diverse}
Kun Huang, Xiao Ma, Zetian Zhang, Yuhan Zhang, Songtao Yuan, Huazhu Fu, and Qiang Chen.
\newblock Diverse data generation for retinal layer segmentation with potential structure modeling.
\newblock \emph{IEEE Transactions on Medical Imaging}, 43\penalty0 (10):\penalty0 3584--3595, 2024.
\newblock \doi{10.1109/TMI.2024.3384484}.

\bibitem[Ilanchezian et~al.(2025)Ilanchezian, Boreiko, K{\"u}hlewein, Huang, Se{\c{c}}kin~Ayhan, Hein, Koch, and Berens]{ilanchezian2025development}
Indu Ilanchezian, Valentyn Boreiko, Laura K{\"u}hlewein, Ziwei Huang, Murat Se{\c{c}}kin~Ayhan, Matthias Hein, Lisa Koch, and Philipp Berens.
\newblock Development and validation of an ai algorithm to generate realistic and meaningful counterfactuals for retinal imaging based on diffusion models.
\newblock \emph{PLOS Digital Health}, 4\penalty0 (5):\penalty0 e0000853, 2025.

\bibitem[Jayasumana et~al.(2024)Jayasumana, Ramalingam, Veit, Glasner, Chakrabarti, and Kumar]{jayasumana2024rethinking}
Sadeep Jayasumana, Srikumar Ramalingam, Andreas Veit, Daniel Glasner, Ayan Chakrabarti, and Sanjiv Kumar.
\newblock Rethinking {FID}: Towards a better evaluation metric for image generation.
\newblock In \emph{Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages 9307--9315, 2024.

\bibitem[Jiralerspong et~al.(2023)Jiralerspong, Bose, Gemp, Qin, Bachrach, and Gidel]{jiralerspong2023feature}
Marco Jiralerspong, Joey Bose, Ian Gemp, Chongli Qin, Yoram Bachrach, and Gauthier Gidel.
\newblock Feature likelihood divergence: Evaluating the generalization of generative models using samples.
\newblock \emph{Advances in Neural Information Processing Systems}, 36:\penalty0 33095--33119, 2023.

\bibitem[Karras et~al.(2021)Karras, Aittala, Laine, H{\"a}rk{\"o}nen, Hellsten, Lehtinen, and Aila]{karras2021alias}
Tero Karras, Miika Aittala, Samuli Laine, Erik H{\"a}rk{\"o}nen, Janne Hellsten, Jaakko Lehtinen, and Timo Aila.
\newblock Alias-free generative adversarial networks.
\newblock \emph{Advances in Neural Information Processing Systems}, 34:\penalty0 852--863, 2021.

\bibitem[Konz et~al.(2024{\natexlab{a}})Konz, Chen, Gu, Dong, and Mazurowski]{konz2024rethinking}
Nicholas Konz, Yuwen Chen, Hanxue Gu, Haoyu Dong, and Maciej~A. Mazurowski.
\newblock Rethinking perceptual metrics for medical image translation.
\newblock In \emph{Medical Imaging with Deep Learning}, 2024{\natexlab{a}}.

\bibitem[Konz et~al.(2024{\natexlab{b}})Konz, Osuala, Verma, Chen, Gu, Dong, Chen, Marshall, Garrucho, Kushibar, Lang, Kim, Grimm, Lewin, Duncan, Schnabel, Diaz, Lekadir, and Mazurowski]{konz2024fr}
Nicholas Konz, Richard Osuala, Preeti Verma, Yuwen Chen, Hanxue Gu, Haoyu Dong, Yaqian Chen, Andrew Marshall, Lidia Garrucho, Kaisar Kushibar, Daniel~M. Lang, Gene~S. Kim, Lars~J. Grimm, John~M. Lewin, James~S. Duncan, Julia~A. Schnabel, Oliver Diaz, Karim Lekadir, and Maciej~A. Mazurowski.
\newblock Fr{\'e}chet radiomic distance ({FRD}): A versatile metric for comparing medical imaging datasets.
\newblock \emph{arXiv preprint arXiv:2412.01496}, 2024{\natexlab{b}}.

\bibitem[Lang et~al.(2021)Lang, Gandelsman, Yarom, Wald, Elidan, Hassidim, Freeman, Isola, Globerson, Irani, and Mosseri]{lang2021explaining}
Oran Lang, Yossi Gandelsman, Michal Yarom, Yoav Wald, Gal Elidan, Avinatan Hassidim, William~T Freeman, Phillip Isola, Amir Globerson, Michal Irani, and Inbar Mosseri.
\newblock Explaining in style: Training a gan to explain a classifier in stylespace.
\newblock In \emph{Proceedings of the IEEE/CVF International Conference on Computer Vision}, pages 693--702, 2021.

\bibitem[Liu et~al.(2018)Liu, Wei, Lu, and Zhou]{cafd2018}
Shaohui Liu, Yi~Wei, Jiwen Lu, and Jie Zhou.
\newblock An improved evaluation framework for generative adversarial networks.
\newblock \emph{arXiv preprint arXiv:1803.07474}, 2018.

\bibitem[Liu et~al.(2021)Liu, Lin, Cao, Hu, Wei, Zhang, Lin, and Guo]{liu2021swin}
Ze~Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo.
\newblock Swin {T}ransformer: Hierarchical vision transformer using shifted windows.
\newblock In \emph{Proceedings of the IEEE/CVF International Conference on Computer Vision}, pages 10012--10022, 2021.

\bibitem[Luzi et~al.(2023)Luzi, Marrero, Wynar, Baraniuk, and Henry]{luzi2023evaluating}
Lorenzo Luzi, Carlos~Ortiz Marrero, Nile Wynar, Richard~G. Baraniuk, and Michael~J. Henry.
\newblock Evaluating generative networks using {G}aussian mixtures of image features.
\newblock In \emph{Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision}, pages 279--288, 2023.

\bibitem[Mei et~al.(2022)Mei, Liu, Robson, Marinelli, Huang, Doshi, Jacobi, Cao, Link, Yang, Wang, Greenspan, Deyer, Fayad, and Yang]{mei2022radimagenet}
Xueyan Mei, Zelong Liu, Philip~M. Robson, Brett Marinelli, Mingqian Huang, Amish Doshi, Adam Jacobi, Chendi Cao, Katherine~E. Link, Thomas Yang, Ying Wang, Hayit Greenspan, Timothy Deyer, Zahi~A. Fayad, and Yang Yang.
\newblock {RadImageNet}: An open radiologic deep learning research dataset for effective transfer learning.
\newblock \emph{Radiology: Artificial Intelligence}, 4\penalty0 (5):\penalty0 e210315, 2022.

\bibitem[M{\"u}ller et~al.(2025)M{\"u}ller, Koch, Lensch, and Berens]{muller2025disentangling}
Sarah M{\"u}ller, Lisa~M. Koch, Hendrik P.~A. Lensch, and Philipp Berens.
\newblock Disentangling representations of retinal images with generative models.
\newblock \emph{Medical Image Analysis}, page 103628, 2025.

\bibitem[M{\"u}ller-Franzes et~al.(2023)M{\"u}ller-Franzes, Niehues, Khader, Arasteh, Haarburger, Kuhl, Wang, Han, Nolte, Nebelung, Kather, and Truhn]{muller2023multimodal}
Gustav M{\"u}ller-Franzes, Jan~Moritz Niehues, Firas Khader, Soroosh~Tayebi Arasteh, Christoph Haarburger, Christiane Kuhl, Tianci Wang, Tianyu Han, Teresa Nolte, Sven Nebelung, Jakob~Nikolas Kather, and Daniel Truhn.
\newblock A multimodal comparison of latent denoising diffusion probabilistic models and generative adversarial networks for medical image synthesis.
\newblock \emph{Scientific Reports}, 13\penalty0 (1):\penalty0 12098, 2023.

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Maxime Oquab, Timoth{\'e}e Darcet, Th{\'e}o Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Mido Assran, Nicolas Ballas, Wojciech Galuba, Russell Howes, Po-Yao Huang, Shang-Wen Li, Ishan Misra, Michael Rabbat, Vasu Sharma, Gabriel Synnaeve, Hu~Xu, Herve Jegou, Julien Mairal, Patrick Labatut, Armand Joulin, and Piotr Bojanowski.
\newblock {DINO}v2: Learning robust visual features without supervision.
\newblock \emph{Transactions on Machine Learning Research}, 2024.

\bibitem[Park et~al.(2019)Park, Liu, Wang, and Zhu]{park2019semantic}
Taesung Park, Ming-Yu Liu, Ting-Chun Wang, and Jun-Yan Zhu.
\newblock Semantic image synthesis with spatially-adaptive normalization.
\newblock In \emph{Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, pages 2337--2346, 2019.

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Gaurav Parmar, Richard Zhang, and Jun-Yan Zhu.
\newblock On aliased resizing and surprising subtleties in {GAN} evaluation.
\newblock In \emph{Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, pages 11410--11420, 2022.

\bibitem[Peyr{\'e} and Cuturi(2019)]{peyre2019computational}
Gabriel Peyr{\'e} and Marco Cuturi.
\newblock Computational optimal transport: With applications to data science.
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Xuebin Qin, Zichen Zhang, Chenyang Huang, Masood Dehghan, Osmar~R. Zaiane, and Martin Jagersand.
\newblock {U2-Net}: Going deeper with nested {U}-structure for salient object detection.
\newblock \emph{Pattern Recognition}, 106:\penalty0 107404, 2020.

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Alec Radford, Jong~Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever.
\newblock Learning transferable visual models from natural language supervision.
\newblock In \emph{International Conference on Machine Learning}, pages 8748--8763, 2021.

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Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bj{\"o}rn Ommer.
\newblock High-resolution image synthesis with latent diffusion models.
\newblock In \emph{Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages 10684--10695, 2022.

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Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi~Chen.
\newblock Improved techniques for training {GAN}s.
\newblock \emph{Advances in Neural Information Processing Systems}, 29:\penalty0 2234--2242, 2016.

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Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna.
\newblock Rethinking the inception architecture for computer vision.
\newblock In \emph{Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, pages 2818--2826, 2016.

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\newblock A note on the evaluation of generative models.
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Aaron {v}an~{d}en Oord, Oriol Vinyals, and Koray Kavukcuoglu.
\newblock Neural discrete representation learning.
\newblock \emph{Advances in Neural Information Processing Systems}, 30:\penalty0 6309--6318, 2017.

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\newblock Markov processes over denumerable products of spaces, describing large systems of automata.
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\newblock Image quality assessment: from error visibility to structural similarity.
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McKell Woodland, Austin Castelo, Mais Al~Taie, Jessica Albuquerque Marques~Silva, Mohamed Eltaher, Frank Mohn, Alexander Shieh, Suprateek Kundu, Joshua~P. Yung, Ankit~B. Patel, and Kristy~K. Brock.
\newblock Feature extraction for generative medical imaging evaluation: New evidence against an evolving trend.
\newblock In \emph{International Conference on Medical Image Computing and Computer-Assisted Intervention}, pages 87--97, 2024.

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Junde Wu, Huihui Fang, Fei Li, Huazhu Fu, Fengbin Lin, Jiongcheng Li, Lexing Huang, Qinji Yu, Sifan Song, Xinxing Xu, Yanyu Xu, Wensai Wang, Lingxiao Wang, Shuai Lu, Huiqi Li, Shihua Huang, Zhichao Lu, Chubin Ou, Xifei Wei, Bingyuan Liu, Riadh Kobbi, Xiaoying Tang, Li~Lin, Qiang Zhou, Qiang Hu, Hrvoje Bogunovic, Jos{\'e}~Ignacio Orlando, Xiulan Zhang, and Yanwu Xu.
\newblock {GAMMA} challenge: {G}laucoma grading from multi-modality images.
\newblock \emph{Medical Image Analysis}, 90, 2023.

\bibitem[Wu et~al.(2024)Wu, He, Eschweiler, Dou, Fan, Mi, Walter, and Stegmaier]{wu2024retinal}
Yuli Wu, Weidong He, Dennis Eschweiler, Ningxin Dou, Zixin Fan, Shengli Mi, Peter Walter, and Johannes Stegmaier.
\newblock Retinal {OCT} synthesis with denoising diffusion probabilistic models for layer segmentation.
\newblock In \emph{IEEE International Symposium on Biomedical Imaging}, pages 1--5, 2024.

\bibitem[Yilmaz et~al.(2024)Yilmaz, Eschweiler, and Stegmaier]{yilmaz2024annotated}
R{\"u}veyda Yilmaz, Dennis Eschweiler, and Johannes Stegmaier.
\newblock Annotated biomedical video generation using denoising diffusion probabilistic models and flow fields.
\newblock In \emph{International Workshop on Simulation and Synthesis in Medical Imaging}, pages 197--207, 2024.

\bibitem[Yilmaz et~al.(2025)Yilmaz, Keven, Wu, and Stegmaier]{yilmaz2024cascaded}
R{\"u}veyda Yilmaz, Kaan Keven, Yuli Wu, and Johannes Stegmaier.
\newblock Cascaded diffusion models for {2D} and {3D} microscopy image synthesis to enhance cell segmentation.
\newblock In \emph{IEEE 22nd International Symposium on Biomedical Imaging}, pages 1--5, 2025.

\bibitem[Zhang et~al.(2023)Zhang, Rao, and Agrawala]{zhang2023adding}
Lvmin Zhang, Anyi Rao, and Maneesh Agrawala.
\newblock Adding conditional control to text-to-image diffusion models.
\newblock In \emph{Proceedings of the IEEE/CVF International Conference on Computer Vision}, pages 3836--3847, 2023.

\bibitem[Zhou et~al.(2023)Zhou, Chia, Wagner, Ayhan, Williamson, Struyven, Liu, Xu, Lozano, Woodward-Court, Kihara, Consortium, Altmann, Lee, Topol, Denniston, Alexander, and Keane]{zhou2023foundation}
Yukun Zhou, Mark~A. Chia, Siegfried~K. Wagner, Murat~S. Ayhan, Dominic~J. Williamson, Robbert~R. Struyven, Timing Liu, Moucheng Xu, Mateo~G. Lozano, Peter Woodward-Court, Yuka Kihara, UK~Biobank Eye \&~Vision Consortium, Andre Altmann, Aaron~Y. Lee, Eric~J. Topol, Alastair~K. Denniston, Daniel~C. Alexander, and Pearse~A. Keane.
\newblock A foundation model for generalizable disease detection from retinal images.
\newblock \emph{Nature}, 622\penalty0 (7981):\penalty0 156--163, 2023.

\end{thebibliography}
