@String(PAMI = {IEEE Trans. Pattern Anal. Mach. Intell.})
@String(IJCV = {Int. J. Comput. Vis.})
@String(CVPR= {IEEE Conf. Comput. Vis. Pattern Recog.})
@String(ICCV= {Int. Conf. Comput. Vis.})
@String(ECCV= {Eur. Conf. Comput. Vis.})
@String(NIPS= {Adv. Neural Inform. Process. Syst.})
@String(ICPR = {Int. Conf. Pattern Recog.})
@String(BMVC= {Brit. Mach. Vis. Conf.})
@String(TOG= {ACM Trans. Graph.})
@String(TIP  = {IEEE Trans. Image Process.})
@String(TVCG  = {IEEE Trans. Vis. Comput. Graph.})
@String(TMM  = {IEEE Trans. Multimedia})
@String(ACMMM= {ACM Int. Conf. Multimedia})
@String(ICME = {Int. Conf. Multimedia and Expo})
@String(ICASSP=	{ICASSP})
@String(ICIP = {IEEE Int. Conf. Image Process.})
@String(ACCV  = {ACCV})
@String(ICLR = {Int. Conf. Learn. Represent.})
@String(IJCAI = {IJCAI})
@String(PR   = {Pattern Recognition})
@String(AAAI = {AAAI})
@String(CVPRW= {IEEE Conf. Comput. Vis. Pattern Recog. Worksh.})
@String(CSVT = {IEEE Trans. Circuit Syst. Video Technol.})

@String(SPL	= {IEEE Sign. Process. Letters})
@String(VR   = {Vis. Res.})
@String(JOV	 = {J. Vis.})
@String(TVC  = {The Vis. Comput.})
@String(JCST  = {J. Comput. Sci. Tech.})
@String(CGF  = {Comput. Graph. Forum})
@String(CVM = {Computational Visual Media})


@String(PAMI  = {IEEE TPAMI})
@String(IJCV  = {IJCV})
@String(CVPR  = {CVPR})
@String(ICCV  = {ICCV})
@String(ECCV  = {ECCV})
@String(NIPS  = {NeurIPS})
@String(ICPR  = {ICPR})
@String(BMVC  =	{BMVC})
@String(TOG   = {ACM TOG})
@String(TIP   = {IEEE TIP})
@String(TVCG  = {IEEE TVCG})
@String(TCSVT = {IEEE TCSVT})
@String(TMM   =	{IEEE TMM})
@String(ACMMM = {ACM MM})
@String(ICME  =	{ICME})
@String(ICASSP=	{ICASSP})
@String(ICIP  = {ICIP})
@String(ACCV  = {ACCV})
@String(ICLR  = {ICLR})
@String(IJCAI = {IJCAI})
@String(PR = {PR})
@String(AAAI = {AAAI})
@String(CVPRW= {CVPRW})
@String(CSVT = {IEEE TCSVT})



@misc{Authors14,
 author = {FirstName LastName},
 title = {The frobnicatable foo filter},
 note = {Face and Gesture submission ID 324. Supplied as supplemental material {\tt fg324.pdf}},
 year = 2014
}

@misc{Authors14b,
 author = {FirstName LastName},
 title = {Frobnication tutorial},
 note = {Supplied as supplemental material {\tt tr.pdf}},
 year = 2014
}

@article{Alpher02,
author = {FirstName Alpher},
title = {Frobnication},
journal = PAMI,
volume = 12,
number = 1,
pages = {234--778},
year = 2002
}

@article{Alpher03,
author = {FirstName Alpher and FirstName Fotheringham-Smythe},
title = {Frobnication revisited},
journal = {Journal of Foo},
volume = 13,
number = 1,
pages = {234--778},
year = 2003
}

@article{Alpher04,
author = {FirstName Alpher and FirstName Fotheringham-Smythe and FirstName Gamow},
title = {Can a machine frobnicate?},
journal = {Journal of Foo},
volume = 14,
number = 1,
pages = {234--778},
year = 2004
}

@inproceedings{Alpher05,
author = {FirstName Alpher and FirstName Gamow},
title = {Can a computer frobnicate?},
booktitle = CVPR,
pages = {234--778},
year = 2005
}




@article{javaid_significance_2022,
	title = {Significance of machine learning in healthcare: Features, pillars and applications},
	volume = {3},
	issn = {2666-6030},
	url = {https://www.sciencedirect.com/science/article/pii/S2666603022000069},
	doi = {10.1016/j.ijin.2022.05.002},
	shorttitle = {Significance of machine learning in healthcare},
	abstract = {Machine Learning ({ML}) applications are making a considerable impact on healthcare. {ML} is a subtype of Artificial Intelligence ({AI}) technology that aims to improve the speed and accuracy of physicians' work. Countries are currently dealing with an overburdened healthcare system with a shortage of skilled physicians, where {AI} provides a big hope. The healthcare data can be used gainfully to identify the optimal trial sample, collect more data points, assess ongoing data from trial participants, and eliminate data-based errors. {ML}-based techniques assist in detecting early indicators of an epidemic or pandemic. This algorithm examines satellite data, news and social media reports, and even video sources to determine whether the sickness will become out of control. Using {ML} for healthcare can open up a world of possibilities in this field. It frees up healthcare providers' time to focus on patient care rather than searching or entering information. This paper studies {ML} and its need in healthcare, and then it discusses the associated features and appropriate pillars of {ML} for healthcare structure. Finally, it identified and discussed the significant applications of {ML} for healthcare. The applications of this technology in healthcare operations can be tremendously advantageous to the organisation. {ML}-based tools are used to provide various treatment alternatives and individualised treatments and improve the overall efficiency of hospitals and healthcare systems while lowering the cost of care. Shortly, {ML} will impact both physicians and hospitals. It will be crucial in developing clinical decision support, illness detection, and personalised treatment approaches to provide the best potential outcomes.},
	pages = {58--73},
	journaltitle = {International Journal of Intelligent Networks},
	shortjournal = {International Journal of Intelligent Networks},
        journal = {International Journal of Intelligent Networks},
	author = {Javaid, Mohd and Haleem, Abid and Pratap Singh, Ravi and Suman, Rajiv and Rab, Shanay},
	urldate = {2024-05-11},
	date = {2022-01-01},
        year = {2022},
	keywords = {Data, Efficiency, Healthcare, Machine learning, Patient outcome, Treatment},
}

@ARTICLE{han_breaking_2020,
  title    = "Breaking medical data sharing boundaries by using synthesized
              radiographs",
  author   = "Han, Tianyu and Nebelung, Sven and Haarburger, Christoph and
              Horst, Nicolas and Reinartz, Sebastian and Merhof, Dorit and
              Kiessling, Fabian and Schulz, Volkmar and Truhn, Daniel",
  abstract = "Computer vision (CV) has the potential to change medicine
              fundamentally. Expert knowledge provided by CV can enhance
              diagnosis. Unfortunately, existing algorithms often remain below
              expectations, as databases used for training are usually too
              small, incomplete, and heterogeneous in quality. Moreover, data
              protection is a serious obstacle to the exchange of data. To
              overcome this limitation, we propose to use generative models
              (GMs) to produce high-resolution synthetic radiographs that do
              not contain any personal identification information. Blinded
              analyses by CV and radiology experts confirmed the high
              similarity of synthesized and real radiographs. The combination
              of pooled GM improves the performance of CV algorithms trained on
              smaller datasets, and the integration of synthesized data into
              patient data repositories can compensate for underrepresented
              disease entities. By integrating federated learning strategies,
              even hospitals with few datasets can contribute to and benefit
              from GM training.",
  journal  = "Sci Adv",
  volume   =  6,
  number   =  49,
  month    =  dec,
  year     =  2020,
  address  = "United States",
  language = "en"
}


@article{kazerouni_diffusion_2023,
	title = {Diffusion models in medical imaging: A comprehensive survey},
	volume = {88},
	issn = {1361-8415},
	url = {https://www.sciencedirect.com/science/article/pii/S1361841523001068},
	doi = {10.1016/j.media.2023.102846},
	shorttitle = {Diffusion models in medical imaging},
	abstract = {Denoising diffusion models, a class of generative models, have garnered immense interest lately in various deep-learning problems. A diffusion probabilistic model defines a forward diffusion stage where the input data is gradually perturbed over several steps by adding Gaussian noise and then learns to reverse the diffusion process to retrieve the desired noise-free data from noisy data samples. Diffusion models are widely appreciated for their strong mode coverage and quality of the generated samples in spite of their known computational burdens. Capitalizing on the advances in computer vision, the field of medical imaging has also observed a growing interest in diffusion models. With the aim of helping the researcher navigate this profusion, this survey intends to provide a comprehensive overview of diffusion models in the discipline of medical imaging. Specifically, we start with an introduction to the solid theoretical foundation and fundamental concepts behind diffusion models and the three generic diffusion modeling frameworks, namely, diffusion probabilistic models, noise-conditioned score networks, and stochastic differential equations. Then, we provide a systematic taxonomy of diffusion models in the medical domain and propose a multi-perspective categorization based on their application, imaging modality, organ of interest, and algorithms. To this end, we cover extensive applications of diffusion models in the medical domain, including image-to-image translation, reconstruction, registration, classification, segmentation, denoising, 2/3D generation, anomaly detection, and other medically-related challenges. Furthermore, we emphasize the practical use case of some selected approaches, and then we discuss the limitations of the diffusion models in the medical domain and propose several directions to fulfill the demands of this field. Finally, we gather the overviewed studies with their available open-source implementations at our {GitHub}.11https://github.com/amirhossein-kz/Awesome-Diffusion-Models-in-Medical-Imaging. We aim to update the relevant latest papers within it regularly.},
	pages = {102846},
	journaltitle = {Medical Image Analysis},
	shortjournal = {Medical Image Analysis},
        journal = {Medical Image Analysis},
	author = {Kazerouni, Amirhossein and Aghdam, Ehsan Khodapanah and Heidari, Moein and Azad, Reza and Fayyaz, Mohsen and Hacihaliloglu, Ilker and Merhof, Dorit},
	urldate = {2024-02-14},
	date = {2023-08-01},
        year = {2023},
	keywords = {Denoising diffusion models, Diffusion models, Generative models, Medical applications, Medical imaging, Noise conditioned score networks, Score-based models, Survey},
}

@article{coyner_synthetic_2022,
	title = {Synthetic Medical Images for Robust, Privacy-Preserving Training of Artificial Intelligence: Application to Retinopathy of Prematurity Diagnosis},
	volume = {2},
	issn = {2666-9145},
	url = {https://www.sciencedirect.com/science/article/pii/S266691452200015X},
	doi = {10.1016/j.xops.2022.100126},
	shorttitle = {Synthetic Medical Images for Robust, Privacy-Preserving Training of Artificial Intelligence},
	abstract = {Purpose
Developing robust artificial intelligence ({AI}) models for medical image analysis requires large quantities of diverse, well-chosen data that can prove challenging to collect because of privacy concerns, disease rarity, or diagnostic label quality. Collecting image-based datasets for retinopathy of prematurity ({ROP}), a potentially blinding disease, suffers from these challenges. Progressively growing generative adversarial networks ({PGANs}) may help, because they can synthesize highly realistic images that may increase both the size and diversity of medical datasets.
Design
Diagnostic validation study of convolutional neural networks ({CNNs}) for plus disease detection, a component of severe {ROP}, using synthetic data.
Participants
Five thousand eight hundred forty-two retinal fundus images ({RFIs}) collected from 963 preterm infants.
Methods
Retinal vessel maps ({RVMs}) were segmented from {RFIs}. {PGANs} were trained to synthesize {RVMs} with normal, pre-plus, or plus disease vasculature. Convolutional neural networks were trained, using real or synthetic {RVMs}, to detect plus disease from 2 real {RVM} test datasets.
Main Outcome Measures
Features of real and synthetic {RVMs} were evaluated using uniform manifold approximation and projection ({UMAP}). Similarities were evaluated at the dataset and feature level using Fréchet inception distance and Euclidean distance, respectively. {CNN} performance was assessed via area under the receiver operating characteristic curve ({AUC}); {AUCs} were compared via bootstrapping and Delong’s test for correlated receiver operating characteristic curves. Confusion matrices were compared using {McNemar}’s chi-square test and Cohen’s κ value.
Results
The {CNN} trained on synthetic {RVMs} showed a significantly higher {AUC} (0.971; P = 0.006 and P = 0.004) and classified plus disease more similarly to a set of 8 international experts (κ = 0.922) than the {CNN} trained on real {RVMs} ({AUC} = 0.934; κ = 0.701). Real and synthetic {RVMs} overlapped, by plus disease diagnosis, on the {UMAP} manifold, showing that synthetic images spanned the disease severity spectrum. Fréchet inception distance and Euclidean distances suggested that real and synthetic {RVMs} were more dissimilar to one another than real {RVMs} were to one another, further suggesting that synthetic {RVMs} were distinct from the training data with respect to privacy considerations.
Conclusions
Synthetic datasets may be useful for training robust medical {AI} models. Furthermore, {PGANs} may be able to synthesize realistic data for use without protected health information concerns.},
	pages = {100126},
	number = {2},
	journaltitle = {Ophthalmology Science},
	shortjournal = {Ophthalmology Science},
	author = {Coyner, Aaron S. and Chen, Jimmy S. and Chang, Ken and Singh, Praveer and Ostmo, Susan and Chan, R. V. Paul and Chiang, Michael F. and Kalpathy-Cramer, Jayashree and Campbell, J. Peter},
	urldate = {2024-02-14},
	date = {2022-06-01},
	keywords = {Artificial intelligence, Deep learning, Generative adversarial network, Retinopathy of prematurity},
}

@misc{shin_medical_2018,
	title = {Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks},
	url = {http://arxiv.org/abs/1807.10225},
	doi = {10.48550/arXiv.1807.10225},
	abstract = {Data diversity is critical to success when training deep learning models. Medical imaging data sets are often imbalanced as pathologic findings are generally rare, which introduces significant challenges when training deep learning models. In this work, we propose a method to generate synthetic abnormal {MRI} images with brain tumors by training a generative adversarial network using two publicly available data sets of brain {MRI}. We demonstrate two unique benefits that the synthetic images provide. First, we illustrate improved performance on tumor segmentation by leveraging the synthetic images as a form of data augmentation. Second, we demonstrate the value of generative models as an anonymization tool, achieving comparable tumor segmentation results when trained on the synthetic data versus when trained on real subject data. Together, these results offer a potential solution to two of the largest challenges facing machine learning in medical imaging, namely the small incidence of pathological findings, and the restrictions around sharing of patient data.},
	number = {{arXiv}:1807.10225},
	publisher = {{arXiv}},
	author = {Shin, Hoo-Chang and Tenenholtz, Neil A. and Rogers, Jameson K. and Schwarz, Christopher G. and Senjem, Matthew L. and Gunter, Jeffrey L. and Andriole, Katherine and Michalski, Mark},
	urldate = {2024-02-14},
	date = {2018-09-13},
	eprinttype = {arxiv},
	eprint = {1807.10225 [cs, stat]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning},
}


@misc{gu_memorization_2023,
	title = {On Memorization in Diffusion Models},
	url = {http://arxiv.org/abs/2310.02664},
	abstract = {Due to their capacity to generate novel and high-quality samples, diffusion models have attracted significant research interest in recent years. Notably, the typical training objective of diffusion models, i.e., denoising score matching, has a closed-form optimal solution that can only generate training data replicating samples. This indicates that a memorization behavior is theoretically expected, which contradicts the common generalization ability of state-of-the-art diffusion models, and thus calls for a deeper understanding. Looking into this, we first observe that memorization behaviors tend to occur on smaller-sized datasets, which motivates our definition of effective model memorization ({EMM}), a metric measuring the maximum size of training data at which a learned diffusion model approximates its theoretical optimum. Then, we quantify the impact of the influential factors on these memorization behaviors in terms of {EMM}, focusing primarily on data distribution, model configuration, and training procedure. Besides comprehensive empirical results identifying the influential factors, we surprisingly find that conditioning training data on uninformative random labels can significantly trigger the memorization in diffusion models. Our study holds practical significance for diffusion model users and offers clues to theoretical research in deep generative models. Code is available at https://github.com/sail-sg/{DiffMemorize}.},
	number = {{arXiv}:2310.02664},
	publisher = {{arXiv}},
	author = {Gu, Xiangming and Du, Chao and Pang, Tianyu and Li, Chongxuan and Lin, Min and Wang, Ye},
	urldate = {2023-11-07},
	date = {2023-10-04},
	eprinttype = {arxiv},
	eprint = {2310.02664 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning},
}

@misc{somepalli_understanding_2023,
	title = {Understanding and Mitigating Copying in Diffusion Models},
	url = {http://arxiv.org/abs/2305.20086},
	doi = {10.48550/arXiv.2305.20086},
	abstract = {Images generated by diffusion models like Stable Diffusion are increasingly widespread. Recent works and even lawsuits have shown that these models are prone to replicating their training data, unbeknownst to the user. In this paper, we first analyze this memorization problem in text-to-image diffusion models. While it is widely believed that duplicated images in the training set are responsible for content replication at inference time, we observe that the text conditioning of the model plays a similarly important role. In fact, we see in our experiments that data replication often does not happen for unconditional models, while it is common in the text-conditional case. Motivated by our findings, we then propose several techniques for reducing data replication at both training and inference time by randomizing and augmenting image captions in the training set.},
	number = {{arXiv}:2305.20086},
	publisher = {{arXiv}},
	author = {Somepalli, Gowthami and Singla, Vasu and Goldblum, Micah and Geiping, Jonas and Goldstein, Tom},
	urldate = {2023-11-07},
	date = {2023-05-31},
	eprinttype = {arxiv},
	eprint = {2305.20086 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Cryptography and Security, Computer Science - Machine Learning},
}

@misc{dar_investigating_2023,
	title = {Investigating Data Memorization in 3D Latent Diffusion Models for Medical Image Synthesis},
	url = {http://arxiv.org/abs/2307.01148},
	doi = {10.48550/arXiv.2307.01148},
	abstract = {Generative latent diffusion models have been established as state-of-the-art in data generation. One promising application is generation of realistic synthetic medical imaging data for open data sharing without compromising patient privacy. Despite the promise, the capacity of such models to memorize sensitive patient training data and synthesize samples showing high resemblance to training data samples is relatively unexplored. Here, we assess the memorization capacity of 3D latent diffusion models on photon-counting coronary computed tomography angiography and knee magnetic resonance imaging datasets. To detect potential memorization of training samples, we utilize self-supervised models based on contrastive learning. Our results suggest that such latent diffusion models indeed memorize training data, and there is a dire need for devising strategies to mitigate memorization.},
	number = {{arXiv}:2307.01148},
	publisher = {{arXiv}},
	author = {Dar, Salman Ul Hassan and Ghanaat, Arman and Kahmann, Jannik and Ayx, Isabelle and Papavassiliu, Theano and Schoenberg, Stefan O. and Engelhardt, Sandy},
	urldate = {2023-10-24},
	date = {2023-07-06},
	eprinttype = {arxiv},
	eprint = {2307.01148 [cs, eess]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing},
}

@misc{akbar_beware_2023,
	title = {Beware of diffusion models for synthesizing medical images -- A comparison with {GANs} in terms of memorizing brain tumor images},
	url = {http://arxiv.org/abs/2305.07644},
	doi = {10.48550/arXiv.2305.07644},
	abstract = {Diffusion models were initially developed for text-to-image generation and are now being utilized to generate high quality synthetic images. Preceded by {GANs}, diffusion models have shown impressive results using various evaluation metrics. However, commonly used metrics such as {FID} and {IS} are not suitable for determining whether diffusion models are simply reproducing the training images. Here we train {StyleGAN} and diffusion models, using {BRATS}20 and {BRATS}21 datasets, to synthesize brain tumor images, and measure the correlation between the synthetic images and all training images. Our results show that diffusion models are much more likely to memorize the training images, especially for small datasets. Researchers should be careful when using diffusion models for medical imaging, if the final goal is to share the synthetic images.},
	number = {{arXiv}:2305.07644},
	publisher = {{arXiv}},
	author = {Akbar, Muhammad Usman and Wang, Wuhao and Eklund, Anders},
	urldate = {2023-10-24},
	date = {2023-05-12},
	eprinttype = {arxiv},
	eprint = {2305.07644 [cs, eess]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing},
}


@misc{dar_unconditional_2024,
	title = {Unconditional Latent Diffusion Models Memorize Patient Imaging Data},
	url = {http://arxiv.org/abs/2402.01054},
	doi = {10.48550/arXiv.2402.01054},
	abstract = {Generative latent diffusion models hold a wide range of applications in the medical imaging domain. A noteworthy application is privacy-preserved open-data sharing by proposing synthetic data as surrogates of real patient data. Despite the promise, these models are susceptible to patient data memorization, where models generate patient data copies instead of novel synthetic samples. This undermines the whole purpose of preserving patient data and may even result in patient re-identification. Considering the importance of the problem, surprisingly it has received relatively little attention in the medical imaging community. To this end, we assess memorization in latent diffusion models for medical image synthesis. We train 2D and 3D latent diffusion models on {CT}, {MR}, and X-ray datasets for synthetic data generation. Afterwards, we examine the amount of training data memorized utilizing self-supervised models and further investigate various factors that can possibly lead to memorization by training models in different settings. We observe a surprisingly large amount of data memorization among all datasets, with up to 41.7\%, 19.6\%, and 32.6\% of the training data memorized in {CT}, {MRI}, and X-ray datasets respectively. Further analyses reveal that increasing training data size and using data augmentation reduce memorization, while over-training enhances it. Overall, our results suggest a call for memorization-informed evaluation of synthetic data prior to open-data sharing.},
	number = {{arXiv}:2402.01054},
	publisher = {{arXiv}},
	author = {Dar, Salman Ul Hassan and Seyfarth, Marvin and Kahmann, Jannik and Ayx, Isabelle and Papavassiliu, Theano and Schoenberg, Stefan O. and Engelhardt, Sandy},
	urldate = {2024-02-14},
	date = {2024-02-01},
	eprinttype = {arxiv},
	eprint = {2402.01054 [cs, eess]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing},
}

@InProceedings{rombach_high-resolution_2022,
    author    = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
    title     = {High-Resolution Image Synthesis With Latent Diffusion Models},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2022},
    pages     = {10684-10695}
}

@inproceedings{blattmann_align_2023,
    title={Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models},
    author={Blattmann, Andreas and Rombach, Robin and Ling, Huan and Dockhorn, Tim and Kim, Seung Wook and Fidler, Sanja and Kreis, Karsten},
    booktitle={IEEE Conference on Computer Vision and Pattern Recognition ({CVPR})},
    year={2023}
}

@InProceedings{pinaya_brain_2022,
author="Pinaya, Walter H. L.
and Tudosiu, Petru-Daniel
and Dafflon, Jessica
and Da Costa, Pedro F.
and Fernandez, Virginia
and Nachev, Parashkev
and Ourselin, Sebastien
and Cardoso, M. Jorge",
editor="Mukhopadhyay, Anirban
and Oksuz, Ilkay
and Engelhardt, Sandy
and Zhu, Dajiang
and Yuan, Yixuan",
title="Brain Imaging Generation with Latent Diffusion Models",
booktitle="Deep Generative Models",
year="2022",
publisher="Springer Nature Switzerland",
address="Cham",
pages="117--126",
abstract="Deep neural networks have brought remarkable breakthroughs in medical image analysis. However, due to their data-hungry nature, the modest dataset sizes in medical imaging projects might be hindering their full potential. Generating synthetic data provides a promising alternative, allowing to complement training datasets and conducting medical image research at a larger scale. Diffusion models recently have caught the attention of the computer vision community by producing photorealistic synthetic images. In this study, we explore using Latent Diffusion Models to generate synthetic images from high-resolution 3D brain images. We used T1w MRI images from the UK Biobank dataset (N = 31,740) to train our models to learn about the probabilistic distribution of brain images, conditioned on covariates, such as age, sex, and brain structure volumes. We found that our models created realistic data, and we could use the conditioning variables to control the data generation effectively. Besides that, we created a synthetic dataset with 100,000 brain images and made it openly available to the scientific community.",
isbn="978-3-031-18576-2"
}



@ARTICLE{khader_denoising_2023,
  title    = "Denoising diffusion probabilistic models for {3D} medical image
              generation",
  author   = "Khader, Firas and M{\"u}ller-Franzes, Gustav and Tayebi Arasteh,
              Soroosh and Han, Tianyu and Haarburger, Christoph and
              Schulze-Hagen, Maximilian and Schad, Philipp and Engelhardt,
              Sandy and Bae{\ss}ler, Bettina and Foersch, Sebastian and
              Stegmaier, Johannes and Kuhl, Christiane and Nebelung, Sven and
              Kather, Jakob Nikolas and Truhn, Daniel",
  abstract = "Recent advances in computer vision have shown promising results
              in image generation. Diffusion probabilistic models have
              generated realistic images from textual input, as demonstrated by
              DALL-E 2, Imagen, and Stable Diffusion. However, their use in
              medicine, where imaging data typically comprises
              three-dimensional volumes, has not been systematically evaluated.
              Synthetic images may play a crucial role in privacy-preserving
              artificial intelligence and can also be used to augment small
              datasets. We show that diffusion probabilistic models can
              synthesize high-quality medical data for magnetic resonance
              imaging (MRI) and computed tomography (CT). For quantitative
              evaluation, two radiologists rated the quality of the synthesized
              images regarding ``realistic image appearance'', ``anatomical
              correctness'', and ``consistency between slices''. Furthermore,
              we demonstrate that synthetic images can be used in
              self-supervised pre-training and improve the performance of
              breast segmentation models when data is scarce (Dice scores, 0.91
              [without synthetic data], 0.95 [with synthetic data]).",
  journal  = "Scientific Reports",
  volume   =  13,
  number   =  1,
  pages    = "7303",
  month    =  may,
  year     =  2023
}

@misc{fernandez_privacy_2023,
	title = {Privacy Distillation: Reducing Re-identification Risk of Multimodal Diffusion Models},
	url = {http://arxiv.org/abs/2306.01322},
	doi = {10.48550/arXiv.2306.01322},
	shorttitle = {Privacy Distillation},
	abstract = {Knowledge distillation in neural networks refers to compressing a large model or dataset into a smaller version of itself. We introduce Privacy Distillation, a framework that allows a text-to-image generative model to teach another model without exposing it to identifiable data. Here, we are interested in the privacy issue faced by a data provider who wishes to share their data via a multimodal generative model. A question that immediately arises is ``How can a data provider ensure that the generative model is not leaking identifiable information about a patient?''. Our solution consists of (1) training a first diffusion model on real data (2) generating a synthetic dataset using this model and filtering it to exclude images with a re-identifiability risk (3) training a second diffusion model on the filtered synthetic data only. We showcase that datasets sampled from models trained with privacy distillation can effectively reduce re-identification risk whilst maintaining downstream performance.},
	number = {{arXiv}:2306.01322},
	publisher = {{arXiv}},
	author = {Fernandez, Virginia and Sanchez, Pedro and Pinaya, Walter Hugo Lopez and Jacenków, Grzegorz and Tsaftaris, Sotirios A. and Cardoso, Jorge},
	urldate = {2023-11-07},
	date = {2023-06-02},
	eprinttype = {arxiv},
	eprint = {2306.01322 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Cryptography and Security, Computer Science - Machine Learning},
}



@misc{pennisi_privacy-preserving_2023,
	title = {A Privacy-Preserving Walk in the Latent Space of Generative Models for Medical Applications},
	url = {http://arxiv.org/abs/2307.02984},
	doi = {10.48550/arXiv.2307.02984},
	abstract = {Generative Adversarial Networks ({GANs}) have demonstrated their ability to generate synthetic samples that match a target distribution. However, from a privacy perspective, using {GANs} as a proxy for data sharing is not a safe solution, as they tend to embed near-duplicates of real samples in the latent space. Recent works, inspired by k-anonymity principles, address this issue through sample aggregation in the latent space, with the drawback of reducing the dataset by a factor of k. Our work aims to mitigate this problem by proposing a latent space navigation strategy able to generate diverse synthetic samples that may support effective training of deep models, while addressing privacy concerns in a principled way. Our approach leverages an auxiliary identity classifier as a guide to non-linearly walk between points in the latent space, minimizing the risk of collision with near-duplicates of real samples. We empirically demonstrate that, given any random pair of points in the latent space, our walking strategy is safer than linear interpolation. We then test our path-finding strategy combined to k-same methods and demonstrate, on two benchmarks for tuberculosis and diabetic retinopathy classification, that training a model using samples generated by our approach mitigate drops in performance, while keeping privacy preservation.},
	number = {{arXiv}:2307.02984},
	publisher = {{arXiv}},
	author = {Pennisi, Matteo and Salanitri, Federica Proietto and Bellitto, Giovanni and Palazzo, Simone and Bagci, Ulas and Spampinato, Concetto},
	urldate = {2024-04-26},
	date = {2023-07-06},
	eprinttype = {arxiv},
	eprint = {2307.02984 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning},
}

@inproceedings{ho_denoising_2020,
author = {Ho, Jonathan and Jain, Ajay and Abbeel, Pieter},
title = {Denoising diffusion probabilistic models},
year = {2020},
isbn = {9781713829546},
publisher = {Curran Associates Inc.},
address = {Red Hook, NY, USA},
abstract = {We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN.},
booktitle = {Proceedings of the 34th International Conference on Neural Information Processing Systems},
articleno = {574},
numpages = {12},
location = {Vancouver, BC, Canada},
series = {NIPS '20}
}

@inproceedings{razavi_generating_2019,
 author = {Razavi, Ali and van den Oord, Aaron and Vinyals, Oriol},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
 pages = {},
 publisher = {Curran Associates, Inc.},
 title = {Generating Diverse High-Fidelity Images with VQ-VAE-2},
 url = {https://proceedings.neurips.cc/paper_files/paper/2019/file/5f8e2fa1718d1bbcadf1cd9c7a54fb8c-Paper.pdf},
 volume = {32},
 year = {2019}
}

@misc{oord_neural_2018,
	title = {Neural Discrete Representation Learning},
	url = {http://arxiv.org/abs/1711.00937},
	doi = {10.48550/arXiv.1711.00937},
	abstract = {Learning useful representations without supervision remains a key challenge in machine learning. In this paper, we propose a simple yet powerful generative model that learns such discrete representations. Our model, the Vector Quantised-Variational {AutoEncoder} ({VQ}-{VAE}), differs from {VAEs} in two key ways: the encoder network outputs discrete, rather than continuous, codes; and the prior is learnt rather than static. In order to learn a discrete latent representation, we incorporate ideas from vector quantisation ({VQ}). Using the {VQ} method allows the model to circumvent issues of "posterior collapse" -- where the latents are ignored when they are paired with a powerful autoregressive decoder -- typically observed in the {VAE} framework. Pairing these representations with an autoregressive prior, the model can generate high quality images, videos, and speech as well as doing high quality speaker conversion and unsupervised learning of phonemes, providing further evidence of the utility of the learnt representations.},
	number = {{arXiv}:1711.00937},
	publisher = {{arXiv}},
	author = {Oord, Aaron van den and Vinyals, Oriol and Kavukcuoglu, Koray},
	urldate = {2024-01-25},
	date = {2018-05-30},
	eprinttype = {arxiv},
	eprint = {1711.00937 [cs]},
	keywords = {Computer Science - Machine Learning},
}

@INPROCEEDINGS {esser_taming_2021,
author = { Esser, Patrick and Rombach, Robin and Ommer, Bjorn },
booktitle = { 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) },
title = {{ Taming Transformers for High-Resolution Image Synthesis }},
year = {2021},
volume = {},
ISSN = {},
pages = {12868-12878},
abstract = { Designed to learn long-range interactions on sequential data, transformers continue to show state-of-the-art results on a wide variety of tasks. In contrast to CNNs, they contain no inductive bias that prioritizes local interactions. This makes them expressive, but also computationally infeasible for long sequences, such as high-resolution images. We demonstrate how combining the effectiveness of the inductive bias of CNNs with the expressivity of transformers enables them to model and thereby synthesize high-resolution images. We show how to (i) use CNNs to learn a context-rich vocabulary of image constituents, and in turn (ii) utilize transformers to efficiently model their composition within high-resolution images. Our approach is readily applied to conditional synthesis tasks, where both non-spatial information, such as object classes, and spatial information, such as segmentations, can control the generated image. In particular, we present the first results on semantically-guided synthesis of megapixel images with transformers. Project page at https://git.io/JLlvY. },
keywords = {Vocabulary;Image segmentation;Computer vision;Image synthesis;Computer architecture;Transformers;Rendering (computer graphics)},
doi = {10.1109/CVPR46437.2021.01268},
url = {https://doi.ieeecomputersociety.org/10.1109/CVPR46437.2021.01268},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
month =Jun}


@InProceedings{ronneberger_u-net_2015,
author="Ronneberger, Olaf
and Fischer, Philipp
and Brox, Thomas",
editor="Navab, Nassir
and Hornegger, Joachim
and Wells, William M.
and Frangi, Alejandro F.",
title="U-Net: Convolutional Networks for Biomedical Image Segmentation",
booktitle="Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015",
year="2015",
publisher="Springer International Publishing",
address="Cham",
pages="234--241",
abstract="There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net.",
isbn="978-3-319-24574-4"
}


@article{mrnet,
title = "Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet",
abstract = "Background: Magnetic resonance imaging (MRI) of the knee is the preferred method for diagnosing knee injuries. However, interpretation of knee MRI is time-intensive and subject to diagnostic error and variability. An automated system for interpreting knee MRI could prioritize high-risk patients and assist clinicians in making diagnoses. Deep learning methods, in being able to automatically learn layers of features, are well suited for modeling the complex relationships between medical images and their interpretations. In this study we developed a deep learning model for detecting general abnormalities and specific diagnoses (anterior cruciate ligament [ACL] tears and meniscal tears) on knee MRI exams. We then measured the effect of providing the model{\textquoteright}s predictions to clinical experts during interpretation. Methods and findings: Our dataset consisted of 1,370 knee MRI exams performed at Stanford University Medical Center between January 1, 2001, and December 31, 2012 (mean age 38.0 years; 569 [41.5%] female patients). The majority vote of 3 musculoskeletal radiologists established reference standard labels on an internal validation set of 120 exams. We developed MRNet, a convolutional neural network for classifying MRI series and combined predictions from 3 series per exam using logistic regression. In detecting abnormalities, ACL tears, and meniscal tears, this model achieved area under the receiver operating characteristic curve (AUC) values of 0.937 (95% CI 0.895, 0.980), 0.965 (95% CI 0.938, 0.993), and 0.847 (95% CI 0.780, 0.914), respectively, on the internal validation set. We also obtained a public dataset of 917 exams with sagittal T1-weighted series and labels for ACL injury from Clinical Hospital Centre Rijeka, Croatia. On the external validation set of 183 exams, the MRNet trained on Stanford sagittal T2-weighted series achieved an AUC of 0.824 (95% CI 0.757, 0.892) in the detection of ACL injuries with no additional training, while an MRNet trained on the rest of the external data achieved an AUC of 0.911 (95% CI 0.864, 0.958). We additionally measured the specificity, sensitivity, and accuracy of 9 clinical experts (7 board-certified general radiologists and 2 orthopedic surgeons) on the internal validation set both with and without model assistance. Using a 2-sided Pearson{\textquoteright}s chi-squared test with adjustment for multiple comparisons, we found no significant differences between the performance of the model and that of unassisted general radiologists in detecting abnormalities. General radiologists achieved significantly higher sensitivity in detecting ACL tears (p-value = 0.002; q-value = 0.019) and significantly higher specificity in detecting meniscal tears (p-value = 0.003; q-value = 0.019). Using a 1-tailed t test on the change in performance metrics, we found that providing model predictions significantly increased clinical experts{\textquoteright} specificity in identifying ACL tears (p-value < 0.001; q-value = 0.006). The primary limitations of our study include lack of surgical ground truth and the small size of the panel of clinical experts. Conclusions: Our deep learning model can rapidly generate accurate clinical pathology classifications of knee MRI exams from both internal and external datasets. Moreover, our results support the assertion that deep learning models can improve the performance of clinical experts during medical imaging interpretation. Further research is needed to validate the model prospectively and to determine its utility in the clinical setting.",
author = "Nicholas Bien and Pranav Rajpurkar and Ball, {Robyn L.} and Jeremy Irvin and Allison Park and Erik Jones and Michael Bereket and Patel, {Bhavik N.} and Yeom, {Kristen W.} and Katie Shpanskaya and Safwan Halabi and Evan Zucker and Gary Fanton and Amanatullah, {Derek F.} and Beaulieu, {Christopher F.} and Riley, {Geoffrey M.} and Stewart, {Russell J.} and Blankenberg, {Francis G.} and Larson, {David B.} and Jones, {Ricky H.} and Langlotz, {Curtis P.} and Ng, {Andrew Y.} and Lungren, {Matthew P.}",
note = "Publisher Copyright: {\textcopyright} 2018 Bien et al. http://creativecommons.org/licenses/by/4.0/.",
year = "2018",
month = nov,
doi = "10.1371/journal.pmed.1002699",
language = "English (US)",
volume = "15",
journal = "PLoS medicine",
issn = "1549-1277",
publisher = "Nature Publishing Group",
number = "11",
}

% The entry below contains non-ASCII chars that could not be converted
% to a LaTeX equivalent.
@ARTICLE{chilamkurthy_development_2018,
  title    = "Deep learning algorithms for detection of critical findings in
              head {CT} scans: a retrospective study",
  author   = "Chilamkurthy, Sasank and Ghosh, Rohit and Tanamala, Swetha and
              Biviji, Mustafa and Campeau, Norbert G and Venugopal, Vasantha
              Kumar and Mahajan, Vidur and Rao, Pooja and Warier, Prashant",
  abstract = "BACKGROUND: Non-contrast head CT scan is the current standard for
              initial imaging of patients with head trauma or stroke symptoms.
              We aimed to develop and validate a set of deep learning
              algorithms for automated detection of the following key findings
              from these scans: intracranial haemorrhage and its types (ie,
              intraparenchymal, intraventricular, subdural, extradural, and
              subarachnoid); calvarial fractures; midline shift; and mass
              effect. METHODS: We retrospectively collected a dataset
              containing 313 318 head CT scans together with their clinical
              reports from around 20 centres in India between Jan 1, 2011, and
              June 1, 2017. A randomly selected part of this dataset (Qure25k
              dataset) was used for validation and the rest was used to develop
              algorithms. An additional validation dataset (CQ500 dataset) was
              collected in two batches from centres that were different from
              those used for the development and Qure25k datasets. We excluded
              postoperative scans and scans of patients younger than 7 years.
              The original clinical radiology report and consensus of three
              independent radiologists were considered as gold standard for the
              Qure25k and CQ500 datasets, respectively. Areas under the
              receiver operating characteristic curves (AUCs) were primarily
              used to assess the algorithms. FINDINGS: The Qure25k dataset
              contained 21 095 scans (mean age 43 years; 9030 [43\%] female
              patients), and the CQ500 dataset consisted of 214 scans in the
              first batch (mean age 43 years; 94 [44\%] female patients) and
              277 scans in the second batch (mean age 52 years; 84 [30\%]
              female patients). On the Qure25k dataset, the algorithms achieved
              an AUC of 0·92 (95\% CI 0·91-0·93) for detecting intracranial
              haemorrhage (0·90 [0·89-0·91] for intraparenchymal, 0·96
              [0·94-0·97] for intraventricular, 0·92 [0·90-0·93] for subdural,
              0·93 [0·91-0·95] for extradural, and 0·90 [0·89-0·92] for
              subarachnoid). On the CQ500 dataset, AUC was 0·94 (0·92-0·97) for
              intracranial haemorrhage (0·95 [0·93-0·98], 0·93 [0·87-1·00],
              0·95 [0·91-0·99], 0·97 [0·91-1·00], and 0·96 [0·92-0·99],
              respectively). AUCs on the Qure25k dataset were 0·92 (0·91-0·94)
              for calvarial fractures, 0·93 (0·91-0·94) for midline shift, and
              0·86 (0·85-0·87) for mass effect, while AUCs on the CQ500 dataset
              were 0·96 (0·92-1·00), 0·97 (0·94-1·00), and 0·92 (0·89-0·95),
              respectively. INTERPRETATION: Our results show that deep learning
              algorithms can accurately identify head CT scan abnormalities
              requiring urgent attention, opening up the possibility to use
              these algorithms to automate the triage process. FUNDING:
              Qure.ai.",
  journal  = "Lancet",
  volume   =  392,
  number   =  10162,
  pages    = "2388--2396",
  month    =  oct,
  year     =  2018,
  address  = "England",
  language = "en"
}

@inproceedings{heusel_gans_2017,
	title = {{GANs} Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium},
	volume = {30},
	url = {https://papers.nips.cc/paper_files/paper/2017/hash/8a1d694707eb0fefe65871369074926d-Abstract.html},
	abstract = {Generative Adversarial Networks ({GANs}) excel at creating realistic images with complex models for which maximum likelihood is infeasible. However, the convergence of {GAN} training has still not been proved. We propose a two time-scale update rule ({TTUR}) for training {GANs} with stochastic gradient descent on arbitrary {GAN} loss functions. {TTUR} has an individual learning rate for both the discriminator and the generator. Using the theory of stochastic approximation, we prove that the {TTUR} converges under mild assumptions to a stationary local Nash equilibrium. The convergence carries over to the popular Adam optimization, for which we prove that it follows the dynamics of a heavy ball with friction and thus prefers flat minima in the objective landscape. For the evaluation of the performance of {GANs} at image generation, we introduce the `Fréchet Inception Distance'' ({FID}) which captures the similarity of generated images to real ones better than the Inception Score. In experiments, {TTUR} improves learning for {DCGANs} and Improved Wasserstein {GANs} ({WGAN}-{GP}) outperforming conventional {GAN} training on {CelebA}, {CIFAR}-10, {SVHN}, {LSUN} Bedrooms, and the One Billion Word Benchmark.},
	booktitle = {Advances in Neural Information Processing Systems},
	publisher = {Curran Associates, Inc.},
	author = {Heusel, Martin and Ramsauer, Hubert and Unterthiner, Thomas and Nessler, Bernhard and Hochreiter, Sepp},
	urldate = {2024-02-09},
	year = {2017},
}



@misc{chen_med3d_2019,
	title = {Med3D: Transfer Learning for 3D Medical Image Analysis},
	url = {http://arxiv.org/abs/1904.00625},
	doi = {10.48550/arXiv.1904.00625},
	shorttitle = {Med3D},
	abstract = {The performance on deep learning is significantly affected by volume of training data. Models pre-trained from massive dataset such as {ImageNet} become a powerful weapon for speeding up training convergence and improving accuracy. Similarly, models based on large dataset are important for the development of deep learning in 3D medical images. However, it is extremely challenging to build a sufficiently large dataset due to difficulty of data acquisition and annotation in 3D medical imaging. We aggregate the dataset from several medical challenges to build 3DSeg-8 dataset with diverse modalities, target organs, and pathologies. To extract general medical three-dimension (3D) features, we design a heterogeneous 3D network called Med3D to co-train multi-domain 3DSeg-8 so as to make a series of pre-trained models. We transfer Med3D pre-trained models to lung segmentation in {LIDC} dataset, pulmonary nodule classification in {LIDC} dataset and liver segmentation on {LiTS} challenge. Experiments show that the Med3D can accelerate the training convergence speed of target 3D medical tasks 2 times compared with model pre-trained on Kinetics dataset, and 10 times compared with training from scratch as well as improve accuracy ranging from 3\% to 20\%. Transferring our Med3D model on state-the-of-art {DenseASPP} segmentation network, in case of single model, we achieve 94.6{\textbackslash}\% Dice coefficient which approaches the result of top-ranged algorithms on the {LiTS} challenge.},
	number = {{arXiv}:1904.00625},
	publisher = {{arXiv}},
	author = {Chen, Sihong and Ma, Kai and Zheng, Yefeng},
	urldate = {2023-11-15},
	date = {2019-07-17},
        year = "2019",
	eprinttype = {arxiv},
	eprint = {1904.00625 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
}

@inproceedings{wang_multiscale_2003,
	title = {Multiscale structural similarity for image quality assessment},
	volume = {2},
	url = {https://ieeexplore.ieee.org/document/1292216/},
	doi = {10.1109/ACSSC.2003.1292216},
	abstract = {The structural similarity image quality paradigm is based on the assumption that the human visual system is highly adapted for extracting structural information from the scene, and therefore a measure of structural similarity can provide a good approximation to perceived image quality. This paper proposes a multiscale structural similarity method, which supplies more flexibility than previous single-scale methods in incorporating the variations of viewing conditions. We develop an image synthesis method to calibrate the parameters that define the relative importance of different scales. Experimental comparisons demonstrate the effectiveness of the proposed method.},
	eventtitle = {The Thrity-Seventh Asilomar Conference on Signals, Systems \& Computers, 2003},
	pages = {1398--1402 Vol.2},
	booktitle = {The Thrity-Seventh Asilomar Conference on Signals, Systems \& Computers, 2003},
	author = {Wang, Z. and Simoncelli, E.P. and Bovik, A.C.},
	urldate = {2023-12-28},
	date = {2003-11},
        year = {2003},
}


@article{lowe_distinctive_2004,
	title = {Distinctive {Image} {Features} from {Scale}-{Invariant} {Keypoints}},
	volume = {60},
	issn = {0920-5691},
	url = {http://link.springer.com/10.1023/B:VISI.0000029664.99615.94},
	doi = {10.1023/B:VISI.0000029664.99615.94},
	language = {en},
	number = {2},
	urldate = {2024-05-20},
	journal = {International Journal of Computer Vision},
	author = {Lowe, David G.},
	month = nov,
	year = {2004},
	pages = {91--110},
}

@inproceedings{
lipman2023flowmatchinggenerativemodeling,
title={Flow Matching for Generative Modeling},
author={Yaron Lipman and Ricky T. Q. Chen and Heli Ben-Hamu and Maximilian Nickel and Matthew Le},
booktitle={The Eleventh International Conference on Learning Representations },
year={2023},
url={https://openreview.net/forum?id=PqvMRDCJT9t}
}


@InProceedings{pmlr-v235-esser24a,
  title = 	 {Scaling Rectified Flow Transformers for High-Resolution Image Synthesis},
  author =       {Esser, Patrick and Kulal, Sumith and Blattmann, Andreas and Entezari, Rahim and M\"{u}ller, Jonas and Saini, Harry and Levi, Yam and Lorenz, Dominik and Sauer, Axel and Boesel, Frederic and Podell, Dustin and Dockhorn, Tim and English, Zion and Rombach, Robin},
  booktitle = 	 {Proceedings of the 41st International Conference on Machine Learning},
  pages = 	 {12606--12633},
  year = 	 {2024},
  editor = 	 {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix},
  volume = 	 {235},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {21--27 Jul},
  publisher =    {PMLR},
  pdf = 	 {https://raw.githubusercontent.com/mlresearch/v235/main/assets/esser24a/esser24a.pdf},
  url = 	 {https://proceedings.mlr.press/v235/esser24a.html},
  abstract = 	 {Diffusion models create data from noise by inverting the forward paths of data towards noise and have emerged as a powerful generative modeling technique for high-dimensional, perceptual data such as images and videos. Rectified flow is a recent generative model formulation that connects data and noise in a straight line. Despite its better theoretical properties and conceptual simplicity, it is not yet decisively established as standard practice. In this work, we improve existing noise sampling techniques for training rectified flow models by biasing them towards perceptually relevant scales. Through a large-scale study, we demonstrate the superior performance of this approach compared to established diffusion formulations for high-resolution text-to-image synthesis. Additionally, we present a novel transformer-based architecture for text-to-image generation that uses separate weights for the two modalities and enables a bidirectional flow of information between image and text tokens, improving text comprehension, typography, and human preference ratings. We demonstrate that this architecture follows predictable scaling trends and correlates lower validation loss to improved text-to-image synthesis as measured by various metrics and human evaluations. Our largest models outperform state-of-the-art models. Stability AI is considering making experimental data, code, and model weights publicly available.}
}


@article{SUBRAMANIAM2022102396,
title = {Generating 3D TOF-MRA volumes and segmentation labels using generative adversarial networks},
journal = {Medical Image Analysis},
volume = {78},
pages = {102396},
year = {2022},
issn = {1361-8415},
doi = {https://doi.org/10.1016/j.media.2022.102396},
url = {https://www.sciencedirect.com/science/article/pii/S1361841522000482},
author = {Pooja Subramaniam and Tabea Kossen and Kerstin Ritter and Anja Hennemuth and Kristian Hildebrand and Adam Hilbert and Jan Sobesky and Michelle Livne and Ivana Galinovic and Ahmed A. Khalil and Jochen B. Fiebach and Dietmar Frey and Vince I. Madai},
keywords = {Generative adversarial networks, 3D Medical imaging, Mixed precision, Anonymization, Brain vessel segmentation},
abstract = {Deep learning requires large labeled datasets that are difficult to gather in medical imaging due to data privacy issues and time-consuming manual labeling. Generative Adversarial Networks (GANs) can alleviate these challenges enabling synthesis of shareable data. While 2D GANs have been used to generate 2D images with their corresponding labels, they cannot capture the volumetric information of 3D medical imaging. 3D GANs are more suitable for this and have been used to generate 3D volumes but not their corresponding labels. One reason might be that synthesizing 3D volumes is challenging owing to computational limitations. In this work, we present 3D GANs for the generation of 3D medical image volumes with corresponding labels applying mixed precision to alleviate computational constraints. We generated 3D Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) patches with their corresponding brain blood vessel segmentation labels. We used four variants of 3D Wasserstein GAN (WGAN) with: 1) gradient penalty (GP), 2) GP with spectral normalization (SN), 3) SN with mixed precision (SN-MP), and 4) SN-MP with double filters per layer (c-SN-MP). The generated patches were quantitatively evaluated using the Fréchet Inception Distance (FID) and Precision and Recall of Distributions (PRD). Further, 3D U-Nets were trained with patch-label pairs from different WGAN models and their performance was compared to the performance of a benchmark U-Net trained on real data. The segmentation performance of all U-Net models was assessed using Dice Similarity Coefficient (DSC) and balanced Average Hausdorff Distance (bAVD) for a) all vessels, and b) intracranial vessels only. Our results show that patches generated with WGAN models using mixed precision (SN-MP and c-SN-MP) yielded the lowest FID scores and the best PRD curves. Among the 3D U-Nets trained with synthetic patch-label pairs, c-SN-MP pairs achieved the highest DSC (0.841) and lowest bAVD (0.508) compared to the benchmark U-Net trained on real data (DSC 0.901; bAVD 0.294) for intracranial vessels. In conclusion, our solution generates realistic 3D TOF-MRA patches and labels for brain vessel segmentation. We demonstrate the benefit of using mixed precision for computational efficiency resulting in the best-performing GAN-architecture. Our work paves the way towards sharing of labeled 3D medical data which would increase generalizability of deep learning models for clinical use.}
}

@ARTICLE{sun_9770375,
  author={Sun, Li and Chen, Junxiang and Xu, Yanwu and Gong, Mingming and Yu, Ke and Batmanghelich, Kayhan},
  journal={IEEE Journal of Biomedical and Health Informatics}, 
  title={Hierarchical Amortized GAN for 3D High Resolution Medical Image Synthesis}, 
  year={2022},
  volume={26},
  number={8},
  pages={3966-3975},
  keywords={Three-dimensional displays;Generative adversarial networks;Feature extraction;Training;Solid modeling;Image resolution;Image reconstruction;3D image synthesis;generative adver- sarial networks;high resolution},
  doi={10.1109/JBHI.2022.3172976}}

@ARTICLE{kim_10452780,
  author={Kim, Jion and Li, Yan and Shin, Byeong-Seok},
  journal={IEEE Journal of Biomedical and Health Informatics}, 
  title={3D-DGGAN: A Data-Guided Generative Adversarial Network for High Fidelity in Medical Image Generation}, 
  year={2024},
  volume={28},
  number={5},
  pages={2904-2915},
  keywords={Three-dimensional displays;Training;Generative adversarial networks;Feature extraction;Neural networks;Image synthesis;Biomedical imaging;Generative adversarial networks;neural networks;biomedical image;image synthesis},
  doi={10.1109/JBHI.2024.3367375}}



@InProceedings{pmlr-v139-nichol21a,
  title = 	 {Improved Denoising Diffusion Probabilistic Models},
  author =       {Nichol, Alexander Quinn and Dhariwal, Prafulla},
  booktitle = 	 {Proceedings of the 38th International Conference on Machine Learning},
  pages = 	 {8162--8171},
  year = 	 {2021},
  editor = 	 {Meila, Marina and Zhang, Tong},
  volume = 	 {139},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {18--24 Jul},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v139/nichol21a/nichol21a.pdf},
  url = 	 {https://proceedings.mlr.press/v139/nichol21a.html},
  abstract = 	 {Denoising diffusion probabilistic models (DDPM) are a class of generative models which have recently been shown to produce excellent samples. We show that with a few simple modifications, DDPMs can also achieve competitive log-likelihoods while maintaining high sample quality. Additionally, we find that learning variances of the reverse diffusion process allows sampling with an order of magnitude fewer forward passes with a negligible difference in sample quality, which is important for the practical deployment of these models. We additionally use precision and recall to compare how well DDPMs and GANs cover the target distribution. Finally, we show that the sample quality and likelihood of these models scale smoothly with model capacity and training compute, making them easily scalable. We release our code and pre-trained models at https://github.com/openai/improved-diffusion.}
}

@inproceedings{friedrich_wdm_2024,
               title={Wdm: 3d wavelet diffusion models for high-resolution medical image synthesis},
               author={Friedrich, Paul and Wolleb, Julia and Bieder, Florentin and Durrer, Alicia and Cattin, Philippe C},
               booktitle={MICCAI Workshop on Deep Generative Models},
               pages={11--21},
               year={2024},
               organization={Springer}}

@ARTICLE{wang20243dmeddiffusion3dmedical,
  title    = "{3D} {MedDiffusion}: A {3D} Medical Latent Diffusion Model for
              Controllable and High-quality Medical Image Generation",
  author   = "Wang, Haoshen and Liu, Zhentao and Sun, Kaicong and Wang,
              Xiaodong and Shen, Dinggang and Cui, Zhiming",
  abstract = "The generation of medical images presents significant challenges
              due to their high-resolution and three-dimensional nature.
              Existing methods often yield suboptimal performance in generating
              high-quality 3D medical images, and there is currently no
              universal generative framework for medical imaging. In this
              paper, we introduce a 3D Medical Latent Diffusion (3D
              MedDiffusion) model for controllable, high-quality 3D medical
              image generation. 3D MedDiffusion incorporates a novel, highly
              efficient Patch-Volume Autoencoder that compresses medical images
              into latent space through patch-wise encoding and recovers back
              into image space through volume-wise decoding. Additionally, we
              design a new noise estimator to capture both local details and
              global structural information during diffusion denoising process.
              3D MedDiffusion can generate fine-detailed, high-resolution
              images (up to 512x512x512) and effectively adapt to various
              downstream tasks as it is trained on large-scale datasets
              covering CT and MRI modalities and different anatomical regions
              (from head to leg). Experimental results demonstrate that 3D
              MedDiffusion surpasses state-of-the-art methods in generative
              quality and exhibits strong generalizability across tasks such as
              sparse-view CT reconstruction, fast MRI reconstruction, and data
              augmentation for segmentationand classification. Source code and
              checkpoints are available at
              https://github.com/ShanghaiTech-IMPACT/3D-MedDiffusion.",
  journal  = "IEEE Trans Med Imaging",
  volume   = "PP",
  month    =  jul,
  year     =  2025,
  address  = "United States",
  language = "en"
}


@article{yazdani_flow_2025,
  title={Flow Matching for Medical Image Synthesis: Bridging the Gap Between Speed and Quality},
  author={Yazdani, Milad and Medghalchi, Yasamin and Ashrafian, Pooria and Hacihaliloglu, Ilker and Shahriari, Dena},
  journal={arXiv preprint arXiv:2503.00266},
  year={2025}
}

@article{10.1145/37402.37422,
author = {Lorensen, William E. and Cline, Harvey E.},
title = {Marching cubes: A high resolution 3D surface construction algorithm},
year = {1987},
issue_date = {July 1987},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {21},
number = {4},
issn = {0097-8930},
url = {https://doi.org/10.1145/37402.37422},
doi = {10.1145/37402.37422},
abstract = {We present a new algorithm, called marching cubes, that creates triangle models of constant density surfaces from 3D medical data. Using a divide-and-conquer approach to generate inter-slice connectivity, we create a case table that defines triangle topology. The algorithm processes the 3D medical data in scan-line order and calculates triangle vertices using linear interpolation. We find the gradient of the original data, normalize it, and use it as a basis for shading the models. The detail in images produced from the generated surface models is the result of maintaining the inter-slice connectivity, surface data, and gradient information present in the original 3D data. Results from computed tomography (CT), magnetic resonance (MR), and single-photon emission computed tomography (SPECT) illustrate the quality and functionality of marching cubes. We also discuss improvements that decrease processing time and add solid modeling capabilities.},
journal = {SIGGRAPH Comput. Graph.},
month = aug,
pages = {163–169},
numpages = {7}
}

@inproceedings{marching_cubes_10.1145/37401.37422,
author = {Lorensen, William E. and Cline, Harvey E.},
title = {Marching cubes: A high resolution 3D surface construction algorithm},
year = {1987},
isbn = {0897912276},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/37401.37422},
doi = {10.1145/37401.37422},
abstract = {We present a new algorithm, called marching cubes, that creates triangle models of constant density surfaces from 3D medical data. Using a divide-and-conquer approach to generate inter-slice connectivity, we create a case table that defines triangle topology. The algorithm processes the 3D medical data in scan-line order and calculates triangle vertices using linear interpolation. We find the gradient of the original data, normalize it, and use it as a basis for shading the models. The detail in images produced from the generated surface models is the result of maintaining the inter-slice connectivity, surface data, and gradient information present in the original 3D data. Results from computed tomography (CT), magnetic resonance (MR), and single-photon emission computed tomography (SPECT) illustrate the quality and functionality of marching cubes. We also discuss improvements that decrease processing time and add solid modeling capabilities.},
booktitle = {Proceedings of the 14th Annual Conference on Computer Graphics and Interactive Techniques},
pages = {163–169},
numpages = {7},
series = {SIGGRAPH '87}
}

@article{kynkäänniemi2019improvedprecisionrecallmetric,
  title={Improved precision and recall metric for assessing generative models},
  author={Kynk{\"a}{\"a}nniemi, Tuomas and Karras, Tero and Laine, Samuli and Lehtinen, Jaakko and Aila, Timo},
  journal={Advances in neural information processing systems},
  volume={32},
  year={2019}
}

@inproceedings{Park_2023,
   title={Probabilistic Precision and Recall Towards Reliable Evaluation of Generative Models},
   url={http://dx.doi.org/10.1109/ICCV51070.2023.01839},
   DOI={10.1109/iccv51070.2023.01839},
   booktitle={2023 IEEE/CVF International Conference on Computer Vision (ICCV)},
   publisher={IEEE},
   author={Park, Dogyun and Kim, Suhyun},
   year={2023},
   month=oct, pages={20042–20052} }

@article{ho2022imagenvideohighdefinition,
  title={Imagen video: High definition video generation with diffusion models},
  author={Ho, Jonathan and Chan, William and Saharia, Chitwan and Whang, Jay and Gao, Ruiqi and Gritsenko, Alexey and Kingma, Diederik P and Poole, Ben and Norouzi, Mohammad and Fleet, David J and others},
  journal={arXiv preprint arXiv:2210.02303},
  year={2022}
}

@article{zhang20233dshape2vecset3dshaperepresentation,
  title={3dshape2vecset: A 3d shape representation for neural fields and generative diffusion models},
  author={Zhang, Biao and Tang, Jiapeng and Niessner, Matthias and Wonka, Peter},
  journal={ACM Transactions On Graphics (TOG)},
  volume={42},
  number={4},
  pages={1--16},
  year={2023},
  publisher={ACM New York, NY, USA}
}

@inproceedings{hui2022neuralwaveletdomaindiffusion3d,
  title={Neural wavelet-domain diffusion for 3d shape generation},
  author={Hui, Ka-Hei and Li, Ruihui and Hu, Jingyu and Fu, Chi-Wing},
  booktitle={SIGGRAPH Asia 2022 conference papers},
  pages={1--9},
  year={2022}
}

@article{ho2022videodiffusionmodels,
  title={Video diffusion models},
  author={Ho, Jonathan and Salimans, Tim and Gritsenko, Alexey and Chan, William and Norouzi, Mohammad and Fleet, David J},
  journal={Advances in neural information processing systems},
  volume={35},
  pages={8633--8646},
  year={2022}
}

@article{mazzocchetti_neural_2024,
	title = {Neural shape completion for personalized Maxillofacial surgery},
	volume = {14},
	rights = {2024 The Author(s)},
	issn = {2045-2322},
	url = {https://www.nature.com/articles/s41598-024-68084-5},
	doi = {10.1038/s41598-024-68084-5},
	abstract = {In this paper, we investigate the effectiveness of shape completion neural networks as clinical aids in maxillofacial surgery planning. We present a pipeline to apply shape completion networks to automatically reconstruct complete eumorphic 3D meshes starting from a partial input mesh, easily obtained from {CT} data routinely acquired for surgery planning. Most of the existing works introduced solutions to aid the design of implants for cranioplasty, i.e. all the defects are located in the neurocranium. In this work, we focus on reconstructing defects localized on both neurocranium and splanchnocranium. To this end, we introduce a new dataset, specifically designed for this task, derived from publicly available {CT} scans and subjected to a comprehensive pre-processing procedure. All the scans in the dataset have been manually cleaned and aligned to a common reference system. In addition, we devised a pre-processing stage to automatically extract point clouds from the scans and enrich them with virtual defects. We experimentally compare several state-of-the-art point cloud completion networks and identify the two most promising models. Finally, expert surgeons evaluated the best-performing network on a clinical case. Our results show how casting the creation of personalized implants as a problem of shape completion is a promising approach for automatizing this complex task.},
	pages = {19810},
	number = {1},
	journaltitle = {Scientific Reports},
        journal = {Scientific Reports},
	shortjournal = {Sci Rep},
	author = {Mazzocchetti, Stefano and Spezialetti, Riccardo and Bevini, Mirko and Badiali, Giovanni and Lisanti, Giuseppe and Salti, Samuele and Di Stefano, Luigi},
	urldate = {2025-07-18},
	date = {2024-08-27},
        year = {2024},
	langid = {english},
	keywords = {Engineering, Medical imaging},
}

@inproceedings{wang2022pointattnneedattentionpoint,
  title={Pointattn: You only need attention for point cloud completion},
  author={Wang, Jun and Cui, Ying and Guo, Dongyan and Li, Junxia and Liu, Qingshan and Shen, Chunhua},
  booktitle={Proceedings of the AAAI Conference on artificial intelligence},
  volume={38(6)},
  pages={5472--5480},
  year={2024}
}

@article{qi2017pointnet++,
  title={Pointnet++: Deep hierarchical feature learning on point sets in a metric space},
  author={Qi, Charles Ruizhongtai and Yi, Li and Su, Hao and Guibas, Leonidas J},
  journal={Advances in neural information processing systems},
  volume={30},
  year={2017}
}

@mastersthesis{amslaurea31647,
           title = {Canonical orientation and segmentation of CT scans of skulls via 3D neural networks},
        abstract = {Point-clouds have emerged as an essential tool, across various fields thanks to their flexibility. As a totally unconstrained data structure, they are used for many purposes, pooled together by the need of capturing, storing and processing 3D models. The use of CAD applications has become a mainstay in a variety of sectors, ranging from architectural design and construction industry to medical field, in particular in maxillofacial surgery, which is beginning to make increasing use of image-guided surgery, virtual surgical planning and the production of 3D printed anatomical models. The currently used approach, in surgical planning, is the direct manual handling of 3D models that requires lots of time and experts skilled in sculptor processing and in CAD modelling. The desired goal is to make this 3D modelling as automatic as possible. The objective of this thesis is about building a part of an overall pipeline, for a bigger research project, that is about the treatment of point-clouds, for a medical application, representing the outer shape of skulls. Specifically, the attention is focused on two steps of the overall 3D processing pipeline, to facilitate it, in the best possible way: the alignment of the skulls in a canonical pose and the separation of the points belonging to the cranium, from those ones of the mandible. The alignment step is dealt with a neural network that is fairly able to recognize the position of the skull in the space and move it, with the aim of placing it in the canonical position, with a rotation error expectation of less than 10? and few millimiters in translation. Regarding the segmentation phase, an ensemble of several models is employed, reaching the 99\% of mean Intersection over Union. In the final step, high-resolution point clouds derived from segmentation are utilized to generate meshes for both the cranium and mandible. This pro- cess employs screened Poisson surface reconstruction, resulting in meticulously detailed meshes.},
          author = {Cialone, Gabriele},
             url = {https://amslaurea.unibo.it/id/eprint/31647/},
        keywords = {Point-clouds,Medical field,Skulls,3D-Pose-estimation,3D-Segmentation},
        year= {2024},
        school = {Università di Bologna}
}

@INPROCEEDINGS{8578166,
  author={Zhang, Richard and Isola, Phillip and Efros, Alexei A. and Shechtman, Eli and Wang, Oliver},
  booktitle={2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition}, 
  title={The Unreasonable Effectiveness of Deep Features as a Perceptual Metric}, 
  year={2018},
  volume={},
  number={},
  pages={586-595},
  keywords={Distortion;Task analysis;Measurement;Visualization;Training;Network architecture;Computer architecture},
  doi={10.1109/CVPR.2018.00068}}

@article{kingma2022autoencodingvariationalbayes,
      title={Auto-Encoding Variational Bayes}, 
      author={Diederik P Kingma and Max Welling},
      year={2014},
      journal={ICLR},
      eprint={1312.6114},
      archivePrefix={arXiv},
      primaryClass={stat.ML}
}

@article{10.1145/gan,
author = {Goodfellow, Ian and Pouget-Abadie, Jean and Mirza, Mehdi and Xu, Bing and Warde-Farley, David and Ozair, Sherjil and Courville, Aaron and Bengio, Yoshua},
title = {Generative adversarial networks},
year = {2020},
issue_date = {November 2020},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {63},
number = {11},
issn = {0001-0782},
url = {https://doi.org/10.1145/3422622},
doi = {10.1145/3422622},
abstract = {Generative adversarial networks are a kind of artificial intelligence algorithm designed to solve the generative modeling problem. The goal of a generative model is to study a collection of training examples and learn the probability distribution that generated them. Generative Adversarial Networks (GANs) are then able to generate more examples from the estimated probability distribution. Generative models based on deep learning are common, but GANs are among the most successful generative models (especially in terms of their ability to generate realistic high-resolution images). GANs have been successfully applied to a wide variety of tasks (mostly in research settings) but continue to present unique challenges and research opportunities because they are based on game theory while most other approaches to generative modeling are based on optimization.},
journal = {Commun. ACM},
month = oct,
pages = {139–144},
numpages = {6}
}


@misc{che2017moderegularizedgenerativeadversarial,
      title={Mode Regularized Generative Adversarial Networks}, 
      author={Tong Che and Yanran Li and Athul Paul Jacob and Yoshua Bengio and Wenjie Li},
      year={2017},
      eprint={1612.02136},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1612.02136}, 
}

@InProceedings{Li_Iterative_MICCAI2024,
        author = { Li, Shuhan and Lin, Yi and Chen, Hao and Cheng, Kwang-Ting},
        title = { { Iterative Online Image Synthesis via Diffusion Model for Imbalanced Classification } },
        booktitle = {proceedings of Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
        year = {2024},
        publisher = {Springer Nature Switzerland},
        volume = {LNCS 15005},
        month = {October},
        page = {371 -- 381}
}