
@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.},
	urldate = {2024-03-14},
	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},
	month = feb,
	year = {2024},
	note = {arXiv: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{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.},
	urldate = {2024-03-14},
	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},
	year = {2017},
}

@inproceedings{mcmahan_communication-efficient_2017,
	title = {Communication-{Efficient} {Learning} of {Deep} {Networks} from {Decentralized} {Data}},
	url = {https://proceedings.mlr.press/v54/mcmahan17a.html},
	abstract = {Modern mobile devices have access to a wealth of data suitable for learning models, which in turn can greatly improve the user experience on the device. For example, language models can improve speech recognition and text entry, and image models can automatically select good photos. However, this rich data is often privacy sensitive, large in quantity, or both, which may preclude logging to the data center and training there using conventional approaches.  We advocate an alternative that leaves the training data distributed on the mobile devices, and learns a shared model by aggregating locally-computed updates. We term this decentralized approach Federated Learning.  We present a practical method for the federated learning of deep networks based on iterative model averaging, and conduct an extensive empirical evaluation, considering five different model architectures and four datasets. These experiments demonstrate the approach is robust to the unbalanced and non-IID data distributions that are a defining characteristic of this setting. Communication costs are the principal constraint, and we show a reduction in required communication rounds by 10-100x as compared to synchronized stochastic gradient descent.},
	language = {en},
	urldate = {2024-03-14},
	booktitle = {Proceedings of the 20th {International} {Conference} on {Artificial} {Intelligence} and {Statistics}},
	publisher = {PMLR},
	author = {McMahan, Brendan and Moore, Eider and Ramage, Daniel and Hampson, Seth and Arcas, Blaise Aguera y},
	month = apr,
	year = {2017},
	note = {ISSN: 2640-3498},
	pages = {1273--1282},
}

@misc{mcmahan_communication-efficient_2023,
	title = {Communication-{Efficient} {Learning} of {Deep} {Networks} from {Decentralized} {Data}},
	url = {http://arxiv.org/abs/1602.05629},
	doi = {10.48550/arXiv.1602.05629},
	abstract = {Modern mobile devices have access to a wealth of data suitable for learning models, which in turn can greatly improve the user experience on the device. For example, language models can improve speech recognition and text entry, and image models can automatically select good photos. However, this rich data is often privacy sensitive, large in quantity, or both, which may preclude logging to the data center and training there using conventional approaches. We advocate an alternative that leaves the training data distributed on the mobile devices, and learns a shared model by aggregating locally-computed updates. We term this decentralized approach Federated Learning. We present a practical method for the federated learning of deep networks based on iterative model averaging, and conduct an extensive empirical evaluation, considering five different model architectures and four datasets. These experiments demonstrate the approach is robust to the unbalanced and non-IID data distributions that are a defining characteristic of this setting. Communication costs are the principal constraint, and we show a reduction in required communication rounds by 10-100x as compared to synchronized stochastic gradient descent.},
	urldate = {2024-03-14},
	publisher = {arXiv},
	author = {McMahan, H. Brendan and Moore, Eider and Ramage, Daniel and Hampson, Seth and Arcas, Blaise Agüera y},
	month = jan,
	year = {2023},
	note = {arXiv:1602.05629 [cs]},
	keywords = {Computer Science - Machine Learning},
}

@book{wilcoxon_individual_1992,
	title = {Individual comparisons by ranking methods},
	publisher = {Springer},
	author = {Wilcoxon, Frank},
	year = {1992},
}

@article{dunn_multiple_1961,
	title = {Multiple comparisons among means},
	volume = {56},
	number = {293},
	journal = {Journal of the American statistical association},
	author = {Dunn, Olive Jean},
	year = {1961},
	note = {Publisher: Taylor \& Francis},
	pages = {52--64},
}

@misc{ravuri_seeing_2019,
	title = {Seeing is {Not} {Necessarily} {Believing}: {Limitations} of {BigGANs} for {Data} {Augmentation}},
	url = {https://openreview.net/forum?id=rJMw747l_4},
	author = {Ravuri, Suman and Vinyals, Oriol},
	year = {2019},
}

@inproceedings{feng_when_2021,
	address = {Montreal, QC, Canada},
	title = {When do {GANs} replicate? {On} the choice of dataset size},
	isbn = {978-1-66542-812-5},
	shorttitle = {When do {GANs} replicate?},
	url = {https://ieeexplore.ieee.org/document/9709947/},
	doi = {10.1109/ICCV48922.2021.00663},
	abstract = {Do GANs replicate training images? Previous studies have shown that GANs do not seem to replicate training data without significant change in the training procedure. This leads to a series of research on the exact condition needed for GANs to overfit to the training data. Although a number of factors has been theoretically or empirically identified, the effect of dataset size and complexity on GANs replication is still unknown. With empirical evidence from BigGAN and StyleGAN2, on datasets CelebA, Flower and LSUN-bedroom, we show that dataset size and its complexity play an important role in GANs replication and perceptual quality of the generated images. We further quantify this relationship, discovering that replication percentage decays exponentially with respect to dataset size and complexity, with a shared decaying factor across GAN-dataset combinations. Meanwhile, the perceptual image quality follows a U-shape trend w.r.t dataset size. This finding leads to a practical tool for one-shot estimation on minimal dataset size to prevent GAN replication which can be used to guide datasets construction and selection.},
	language = {en},
	urldate = {2024-02-07},
	booktitle = {2021 {IEEE}/{CVF} {International} {Conference} on {Computer} {Vision} ({ICCV})},
	publisher = {IEEE},
	author = {Feng, Qianli and Guo, Chenqi and Benitez-Quiroz, Fabian and Martinez, Aleix},
	month = oct,
	year = {2021},
	pages = {6681--6690},
}

@article{bernal_wm-dova_2015,
	title = {{WM}-{DOVA} maps for accurate polyp highlighting in colonoscopy: {Validation} vs. saliency maps from physicians},
	volume = {43},
	issn = {0895-6111},
	shorttitle = {{WM}-{DOVA} maps for accurate polyp highlighting in colonoscopy},
	url = {https://www.sciencedirect.com/science/article/pii/S0895611115000567},
	doi = {10.1016/j.compmedimag.2015.02.007},
	abstract = {We introduce in this paper a novel polyp localization method for colonoscopy videos. Our method is based on a model of appearance for polyps which defines polyp boundaries in terms of valley information. We propose the integration of valley information in a robust way fostering complete, concave and continuous boundaries typically associated to polyps. This integration is done by using a window of radial sectors which accumulate valley information to create WM-DOVA (Window Median Depth of Valleys Accumulation) energy maps related with the likelihood of polyp presence. We perform a double validation of our maps, which include the introduction of two new databases, including the first, up to our knowledge, fully annotated database with clinical metadata associated. First we assess that the highest value corresponds with the location of the polyp in the image. Second, we show that WM-DOVA energy maps can be comparable with saliency maps obtained from physicians’ fixations obtained via an eye-tracker. Finally, we prove that our method outperforms state-of-the-art computational saliency results. Our method shows good performance, particularly for small polyps which are reported to be the main sources of polyp miss-rate, which indicates the potential applicability of our method in clinical practice.},
	urldate = {2024-02-04},
	journal = {Computerized Medical Imaging and Graphics},
	author = {Bernal, Jorge and Sánchez, F. Javier and Fernández-Esparrach, Gloria and Gil, Debora and Rodríguez, Cristina and Vilariño, Fernando},
	month = jul,
	year = {2015},
	keywords = {Colonoscopy, Energy maps, Polyp localization, Saliency, Valley detection},
	pages = {99--111},
}

@article{schwarz_identification_2019,
	title = {Identification of {Anonymous} {MRI} {Research} {Participants} with {Face}-{Recognition} {Software}},
	volume = {381},
	issn = {0028-4793},
	url = {https://doi.org/10.1056/NEJMc1908881},
	doi = {10.1056/NEJMc1908881},
	number = {17},
	urldate = {2024-02-04},
	journal = {New England Journal of Medicine},
	author = {Schwarz, Christopher G. and Kremers, Walter K. and Therneau, Terry M. and Sharp, Richard R. and Gunter, Jeffrey L. and Vemuri, Prashanthi and Arani, Arvin and Spychalla, Anthony J. and Kantarci, Kejal and Knopman, David S. and Petersen, Ronald C. and Jack, Clifford R.},
	month = oct,
	year = {2019},
	pmid = {31644852},
	note = {Publisher: Massachusetts Medical Society
\_eprint: https://doi.org/10.1056/NEJMc1908881},
	pages = {1684--1686},
}

@article{borgli_hyperkvasir_2020,
	title = {{HyperKvasir}, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy},
	volume = {7},
	issn = {2052-4463},
	url = {https://doi.org/10.1038/s41597-020-00622-y},
	doi = {10.1038/s41597-020-00622-y},
	number = {1},
	journal = {Scientific Data},
	author = {Borgli, Hanna and Thambawita, Vajira and Smedsrud, Pia H and Hicks, Steven and Jha, Debesh and Eskeland, Sigrun L and Randel, Kristin Ranheim and Pogorelov, Konstantin and Lux, Mathias and Nguyen, Duc Tien Dang and Johansen, Dag and Griwodz, Carsten and Stensland, Håkon K and Garcia-Ceja, Enrique and Schmidt, Peter T and Hammer, Hugo L and Riegler, Michael A and Halvorsen, Pål and de Lange, Thomas},
	year = {2020},
	pages = {283},
}

@inproceedings{degerli_osegnet_2022,
	title = {Osegnet: {Operational} {Segmentation} {Network} for {Covid}-19 {Detection} {Using} {Chest} {X}-{Ray} {Images}},
	shorttitle = {Osegnet},
	url = {https://ieeexplore.ieee.org/document/9897412},
	doi = {10.1109/ICIP46576.2022.9897412},
	abstract = {Coronavirus disease 2019 (COVID-19) has been diagnosed automatically using Machine Learning algorithms over chest X-ray (CXR) images. However, most of the earlier studies used Deep Learning models over scarce datasets bearing the risk of overfitting. Additionally, previous studies have revealed the fact that deep networks are not reliable for classification since their decisions may originate from irrelevant areas on the CXRs. Therefore, in this study, we propose Operational Segmentation Network (OSegNet) that performs detection by segmenting COVID-19 pneumonia for a reliable diagnosis. To address the data scarcity encountered in training and especially in evaluation, this study extends the largest COVID-19 CXR dataset: QaTa-COV19 with 121,378 CXRs including 9258 COVID-19 samples with their corresponding ground-truth segmentation masks that are publicly shared with the research community. Consequently, OSegNet has achieved a detection performance with the highest accuracy of 99.65\% among the state-of-the-art deep models with 98.09\% precision.},
	urldate = {2024-01-30},
	booktitle = {2022 {IEEE} {International} {Conference} on {Image} {Processing} ({ICIP})},
	author = {Degerli, Aysen and Kiranyaz, Serkan and Chowdhury, Muhammad E. H. and Gabbouj, Moncef},
	month = oct,
	year = {2022},
	note = {ISSN: 2381-8549},
	keywords = {COVID-19, Computer network reliability, Deep Learning, Image segmentation, Machine Learning, Machine learning algorithms, Pulmonary diseases, SARS-CoV-2, Sensitivity, Training},
	pages = {2306--2310},
}

@inproceedings{cherti_reproducible_2023,
	title = {Reproducible scaling laws for contrastive language-image learning},
	booktitle = {Proceedings of the {IEEE}/{CVF} {Conference} on {Computer} {Vision} and {Pattern} {Recognition}},
	author = {Cherti, Mehdi and Beaumont, Romain and Wightman, Ross and Wortsman, Mitchell and Ilharco, Gabriel and Gordon, Cade and Schuhmann, Christoph and Schmidt, Ludwig and Jitsev, Jenia},
	year = {2023},
	pages = {2818--2829},
}

@misc{ilharco_openclip_2021,
	title = {{OpenCLIP}},
	url = {https://doi.org/10.5281/zenodo.5143773},
	publisher = {Zenodo},
	author = {Ilharco, Gabriel and Wortsman, Mitchell and Wightman, Ross and Gordon, Cade and Carlini, Nicholas and Taori, Rohan and Dave, Achal and Shankar, Vaishaal and Namkoong, Hongseok and Miller, John and Hajishirzi, Hannaneh and Farhadi, Ali and Schmidt, Ludwig},
	month = jul,
	year = {2021},
	doi = {10.5281/zenodo.5143773},
}

@inproceedings{mosbach_stability_2020,
	title = {On the {Stability} of {Fine}-tuning {BERT}: {Misconceptions}, {Explanations}, and {Strong} {Baselines}},
	shorttitle = {On the {Stability} of {Fine}-tuning {BERT}},
	url = {https://openreview.net/forum?id=nzpLWnVAyah&utm_campaign=NLP%20News&utm_medium=email&utm_source=Revue%20newsletter},
	abstract = {Fine-tuning pre-trained transformer-based language models such as BERT has become a common practice dominating leaderboards across various NLP benchmarks. Despite the strong empirical performance of fine-tuned models, fine-tuning is an unstable process: training the same model with multiple random seeds can result in a large variance of the task performance. Previous literature (Devlin et al., 2019; Lee et al., 2020; Dodge et al., 2020) identified two potential reasons for the observed instability: catastrophic forgetting and small size of the fine-tuning datasets. In this paper, we show that both hypotheses fail to explain the fine-tuning instability. We analyze BERT, RoBERTa, and ALBERT, fine-tuned on commonly used datasets from the GLUE benchmark, and show that the observed instability is caused by optimization difficulties that lead to vanishing gradients. Additionally, we show that the remaining variance of the downstream task performance can be attributed to differences in generalization where fine-tuned models with the same training loss exhibit noticeably different test performance. Based on our analysis, we present a simple but strong baseline that makes fine-tuning BERT-based models significantly more stable than the previously proposed approaches. Code to reproduce our results is available online: https://github.com/uds-lsv/bert-stable-fine-tuning.},
	language = {en},
	urldate = {2024-01-30},
	author = {Mosbach, Marius and Andriushchenko, Maksym and Klakow, Dietrich},
	month = oct,
	year = {2020},
}

@inproceedings{zhao_differentiable_2020,
	title = {Differentiable {Augmentation} for {Data}-{Efficient} {GAN} {Training}},
	volume = {33},
	url = {https://proceedings.neurips.cc/paper/2020/hash/55479c55ebd1efd3ff125f1337100388-Abstract.html},
	abstract = {The performance of generative adversarial networks (GANs) heavily deteriorates given a limited amount of training data. This is mainly because the discriminatorsis memorizing the exact training set. To combat it, we propose Differentiable Augmentation (DiffAugment), a simple method that improves the data efficiency of GANs by imposing various types of differentiable augmentations on both real and fake samples. Previous attempts to directly augment the training data manipulate the distribution of real images, yielding little benefit; DiffAugment enables us to adopt the differentiable augmentation for the generated samples, effectively stabilizes training, and leads to better convergence. Experiments demonstrate consistent gains of our method over a variety of GAN architectures and loss functions for both unconditional and class-conditional generation. With DiffAugment, we achieve astate-of-the-art FID of 6.80 with an IS of 100.8 on ImageNet 128×128 and 2-4× reductions of FID given 1,000 images on FFHQ and LSUN. Furthermore, with only 20\% training data, we can match the top performance on CIFAR-10 and CIFAR-100. Finally, our method can generate high-fidelity images using only 100 images without pre-training, while being on par with existing transfer learning algorithms. Code is available at https://github.com/mit-han-lab/data-efficient-gans.},
	urldate = {2024-01-28},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems}},
	publisher = {Curran Associates, Inc.},
	author = {Zhao, Shengyu and Liu, Zhijian and Lin, Ji and Zhu, Jun-Yan and Han, Song},
	year = {2020},
	pages = {7559--7570},
}

@inproceedings{karras_analyzing_2020,
	title = {Analyzing and {Improving} the {Image} {Quality} of {StyleGAN}},
	isbn = {978-1-72817-168-5},
	url = {https://www.computer.org/csdl/proceedings-article/cvpr/2020/716800i107/1m3nK5FMmWI},
	doi = {10.1109/CVPR42600.2020.00813},
	abstract = {The style-based GAN architecture (StyleGAN) yields state-of-the-art results in data-driven unconditional generative image modeling. We expose and analyze several of its characteristic artifacts, and propose changes in both model architecture and training methods to address them. In particular, we redesign the generator normalization, revisit progressive growing, and regularize the generator to encourage good conditioning in the mapping from latent codes to images. In addition to improving image quality, this path length regularizer yields the additional benefit that the generator becomes significantly easier to invert. This makes it possible to reliably attribute a generated image to a particular network. We furthermore visualize how well the generator utilizes its output resolution, and identify a capacity problem, motivating us to train larger models for additional quality improvements. Overall, our improved model redefines the state of the art in unconditional image modeling, both in terms of existing distribution quality metrics as well as perceived image quality.},
	language = {English},
	urldate = {2024-01-28},
	publisher = {IEEE Computer Society},
	author = {Karras, Tero and Laine, Samuli and Aittala, Miika and Hellsten, Janne and Lehtinen, Jaakko and Aila, Timo},
	month = jun,
	year = {2020},
	pages = {8107--8116},
}

@inproceedings{woodland_evaluating_2022,
	address = {Cham},
	series = {Lecture {Notes} in {Computer} {Science}},
	title = {Evaluating the {Performance} of {StyleGAN2}-{ADA} on {Medical} {Images}},
	isbn = {978-3-031-16980-9},
	doi = {10.1007/978-3-031-16980-9_14},
	abstract = {Although generative adversarial networks (GANs) have shown promise in medical imaging, they have four main limitations that impede their utility: computational cost, data requirements, reliable evaluation measures, and training complexity. Our work investigates each of these obstacles in a novel application of StyleGAN2-ADA to high-resolution medical imaging datasets. Our dataset is comprised of liver-containing axial slices from non-contrast and contrast-enhanced computed tomography (CT) scans. Additionally, we utilized four public datasets composed of various imaging modalities. We trained a StyleGAN2 network with transfer learning (from the Flickr-Faces-HQ dataset) and data augmentation (horizontal flipping and adaptive discriminator augmentation). The network’s generative quality was measured quantitatively with the Fréchet Inception Distance (FID) and qualitatively with a visual Turing test given to seven radiologists and radiation oncologists.},
	language = {en},
	booktitle = {Simulation and {Synthesis} in {Medical} {Imaging}},
	publisher = {Springer International Publishing},
	author = {Woodland, McKell and Wood, John and Anderson, Brian M. and Kundu, Suprateek and Lin, Ethan and Koay, Eugene and Odisio, Bruno and Chung, Caroline and Kang, Hyunseon Christine and Venkatesan, Aradhana M. and Yedururi, Sireesha and De, Brian and Lin, Yuan-Mao and Patel, Ankit B. and Brock, Kristy K.},
	editor = {Zhao, Can and Svoboda, David and Wolterink, Jelmer M. and Escobar, Maria},
	year = {2022},
	keywords = {Data augmentation, Fréchet Inception Distance, StyleGAN2-ADA, Transfer learning, Visual turing test},
	pages = {142--153},
}

@inproceedings{ronneberger_u-net_2015,
	address = {Cham},
	series = {Lecture {Notes} in {Computer} {Science}},
	title = {U-{Net}: {Convolutional} {Networks} for {Biomedical} {Image} {Segmentation}},
	isbn = {978-3-319-24574-4},
	shorttitle = {U-{Net}},
	doi = {10.1007/978-3-319-24574-4_28},
	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.},
	language = {en},
	booktitle = {Medical {Image} {Computing} and {Computer}-{Assisted} {Intervention} – {MICCAI} 2015},
	publisher = {Springer International Publishing},
	author = {Ronneberger, Olaf and Fischer, Philipp and Brox, Thomas},
	editor = {Navab, Nassir and Hornegger, Joachim and Wells, William M. and Frangi, Alejandro F.},
	year = {2015},
	keywords = {Convolutional Layer, Data Augmentation, Deep Network, Ground Truth Segmentation, Training Image},
	pages = {234--241},
}

@incollection{navab_u-net_2015,
	address = {Cham},
	title = {U-{Net}: {Convolutional} {Networks} for {Biomedical} {Image} {Segmentation}},
	volume = {9351},
	isbn = {978-3-319-24573-7 978-3-319-24574-4},
	shorttitle = {U-{Net}},
	url = {http://link.springer.com/10.1007/978-3-319-24574-4_28},
	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.},
	language = {en},
	urldate = {2024-01-17},
	booktitle = {Medical {Image} {Computing} and {Computer}-{Assisted} {Intervention} – {MICCAI} 2015},
	publisher = {Springer International Publishing},
	author = {Ronneberger, Olaf and Fischer, Philipp and Brox, Thomas},
	editor = {Navab, Nassir and Hornegger, Joachim and Wells, William M. and Frangi, Alejandro F.},
	year = {2015},
	doi = {10.1007/978-3-319-24574-4_28},
	note = {Series Title: Lecture Notes in Computer Science},
	pages = {234--241},
}

@inproceedings{zhu_deep_2019,
	title = {Deep {Leakage} from {Gradients}},
	volume = {32},
	url = {https://proceedings.neurips.cc/paper/2019/hash/60a6c4002cc7b29142def8871531281a-Abstract.html},
	abstract = {Passing gradient is a widely used scheme in  modern multi-node learning system (e.g, distributed training, collaborative learning). In a long time, people used to believe that gradients are safe to share: i.e, the training set will not be leaked by gradient sharing.  However, in this paper, we show that we can obtain the private training set from the publicly shared gradients.  The leaking only takes few gradient steps to process and can obtain the original training set instead of look-alike alternatives.  We name this leakage as {\textbackslash}textit\{deep leakage from gradient\}  and practically validate the effectiveness of our algorithm on both computer vision and natural language processing tasks. We empirically show that our attack is much stronger than previous approaches and thereby and raise people's awareness to rethink the gradients' safety. We also discuss some possible strategies to defend this deep leakage.},
	urldate = {2024-01-17},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems}},
	publisher = {Curran Associates, Inc.},
	author = {Zhu, Ligeng and Liu, Zhijian and Han, Song},
	year = {2019},
}

@inproceedings{chang_synthetic_2020,
	title = {Synthetic {Learning}: {Learn} {From} {Distributed} {Asynchronized} {Discriminator} {GAN} {Without} {Sharing} {Medical} {Image} {Data}},
	shorttitle = {Synthetic {Learning}},
	url = {https://openaccess.thecvf.com/content_CVPR_2020/html/Chang_Synthetic_Learning_Learn_From_Distributed_Asynchronized_Discriminator_GAN_Without_Sharing_CVPR_2020_paper.html},
	urldate = {2024-01-17},
	author = {Chang, Qi and Qu, Hui and Zhang, Yikai and Sabuncu, Mert and Chen, Chao and Zhang, Tong and Metaxas, Dimitris N.},
	year = {2020},
	pages = {13856--13866},
}

@article{wang_fedmed-gan_2023,
	title = {{FedMed}-{GAN}: {Federated} domain translation on unsupervised cross-modality brain image synthesis},
	volume = {546},
	issn = {0925-2312},
	shorttitle = {{FedMed}-{GAN}},
	url = {https://www.sciencedirect.com/science/article/pii/S0925231223004058},
	doi = {10.1016/j.neucom.2023.126282},
	abstract = {Utilizing multi-modal neuroimaging data is proven to be effective in investigating human cognitive activities and certain pathologies. However, it is not practical to obtain the full set of paired neuroimaging data centrally since the collection faces several constraints, e.g., high examination cost, long acquisition time, and image corruption. In addition, these data are dispersed into different medical institutions and thus cannot be aggregated for centralized training considering the privacy issues. There is a clear need to launch federated learning and facilitate the integration of dispersed data from different institutions. In this paper, we propose a new benchmark for federated domain translation on unsupervised brain image synthesis (FedMed-GAN) to bridge the gap between federated learning and medical GAN. FedMed-GAN mitigates the mode collapse without sacrificing the performance of generators, and is widely applied to different proportions of unpaired and paired data with variation adaptation properties. We treat the gradient penalties using the federated averaging algorithm and then leverage the differential privacy gradient descent to regularize the training dynamics. A comprehensive evaluation is provided for comparing FedMed-GAN and other centralized methods, demonstrating that the proposed algorithm outperforms the state-of-the-art. Our code is available at: https://github.com/M-3LAB/FedMed-GAN.},
	urldate = {2024-01-17},
	journal = {Neurocomputing},
	author = {Wang, Jinbao and Xie, Guoyang and Huang, Yawen and Lyu, Jiayi and Zheng, Feng and Zheng, Yefeng and Jin, Yaochu},
	month = aug,
	year = {2023},
	keywords = {Brain image, Cross-modality synthesis, Deep learning, Federated learning, Unsupervised learning},
	pages = {126282},
}

@misc{rasouli_fedgan_2020,
	title = {{FedGAN}: {Federated} {Generative} {Adversarial} {Networks} for {Distributed} {Data}},
	shorttitle = {{FedGAN}},
	url = {http://arxiv.org/abs/2006.07228},
	doi = {10.48550/arXiv.2006.07228},
	abstract = {We propose Federated Generative Adversarial Network (FedGAN) for training a GAN across distributed sources of non-independent-and-identically-distributed data sources subject to communication and privacy constraints. Our algorithm uses local generators and discriminators which are periodically synced via an intermediary that averages and broadcasts the generator and discriminator parameters. We theoretically prove the convergence of FedGAN with both equal and two time-scale updates of generator and discriminator, under standard assumptions, using stochastic approximations and communication efficient stochastic gradient descents. We experiment FedGAN on toy examples (2D system, mixed Gaussian, and Swiss role), image datasets (MNIST, CIFAR-10, and CelebA), and time series datasets (household electricity consumption and electric vehicle charging sessions). We show FedGAN converges and has similar performance to general distributed GAN, while reduces communication complexity. We also show its robustness to reduced communications.},
	urldate = {2024-01-17},
	publisher = {arXiv},
	author = {Rasouli, Mohammad and Sun, Tao and Rajagopal, Ram},
	month = jun,
	year = {2020},
	note = {arXiv:2006.07228 [cs, stat]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Computer Science - Multiagent Systems, Statistics - Machine Learning},
}

@article{adnan_federated_2022,
	title = {Federated learning and differential privacy for medical image analysis},
	volume = {12},
	copyright = {2022 The Author(s)},
	issn = {2045-2322},
	url = {https://www.nature.com/articles/s41598-022-05539-7},
	doi = {10.1038/s41598-022-05539-7},
	abstract = {The artificial intelligence revolution has been spurred forward by the availability of large-scale datasets. In contrast, the paucity of large-scale medical datasets hinders the application of machine learning in healthcare. The lack of publicly available multi-centric and diverse datasets mainly stems from confidentiality and privacy concerns around sharing medical data. To demonstrate a feasible path forward in medical image imaging, we conduct a case study of applying a differentially private federated learning framework for analysis of histopathology images, the largest and perhaps most complex medical images. We study the effects of IID and non-IID distributions along with the number of healthcare providers, i.e., hospitals and clinics, and the individual dataset sizes, using The Cancer Genome Atlas (TCGA) dataset, a public repository, to simulate a distributed environment. We empirically compare the performance of private, distributed training to conventional training and demonstrate that distributed training can achieve similar performance with strong privacy guarantees. We also study the effect of different source domains for histopathology images by evaluating the performance using external validation. Our work indicates that differentially private federated learning is a viable and reliable framework for the collaborative development of machine learning models in medical image analysis.},
	language = {en},
	number = {1},
	urldate = {2024-01-17},
	journal = {Scientific Reports},
	author = {Adnan, Mohammed and Kalra, Shivam and Cresswell, Jesse C. and Taylor, Graham W. and Tizhoosh, Hamid R.},
	month = feb,
	year = {2022},
	note = {Number: 1
Publisher: Nature Publishing Group},
	keywords = {Biomedical engineering, Cancer imaging, Pathology},
	pages = {1953},
}

@misc{konecny_federated_2016,
	title = {Federated {Optimization}: {Distributed} {Machine} {Learning} for {On}-{Device} {Intelligence}},
	shorttitle = {Federated {Optimization}},
	url = {http://arxiv.org/abs/1610.02527},
	doi = {10.48550/arXiv.1610.02527},
	abstract = {We introduce a new and increasingly relevant setting for distributed optimization in machine learning, where the data defining the optimization are unevenly distributed over an extremely large number of nodes. The goal is to train a high-quality centralized model. We refer to this setting as Federated Optimization. In this setting, communication efficiency is of the utmost importance and minimizing the number of rounds of communication is the principal goal. A motivating example arises when we keep the training data locally on users' mobile devices instead of logging it to a data center for training. In federated optimziation, the devices are used as compute nodes performing computation on their local data in order to update a global model. We suppose that we have extremely large number of devices in the network --- as many as the number of users of a given service, each of which has only a tiny fraction of the total data available. In particular, we expect the number of data points available locally to be much smaller than the number of devices. Additionally, since different users generate data with different patterns, it is reasonable to assume that no device has a representative sample of the overall distribution. We show that existing algorithms are not suitable for this setting, and propose a new algorithm which shows encouraging experimental results for sparse convex problems. This work also sets a path for future research needed in the context of {\textbackslash}federated optimization.},
	urldate = {2024-01-17},
	publisher = {arXiv},
	author = {Konečný, Jakub and McMahan, H. Brendan and Ramage, Daniel and Richtárik, Peter},
	month = oct,
	year = {2016},
	note = {arXiv:1610.02527 [cs]},
	keywords = {Computer Science - Machine Learning},
}

@article{ng_federated_2021,
	title = {Federated learning: a collaborative effort to achieve better medical imaging models for individual sites that have small labelled datasets},
	volume = {11},
	issn = {2223-4292},
	shorttitle = {Federated learning},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7779924/},
	doi = {10.21037/qims-20-595},
	abstract = {Despite the overall success of using artificial intelligence (AI) to assist radiologists in performing computer-aided patient diagnosis, it remains challenging to build good models with small datasets at individual sites. Because many medical images do not come with proper labelling for training, this requires radiologists to perform strenuous labelling work and to prepare the dataset for training. Placing such demands on radiologists is unsustainable, given the ever-increasing number of medical images taken each year. We propose an alternative solution using a relatively new learning framework. This framework, called federated learning, allows individual sites to train a global model in a collaborative effort. Federated learning involves aggregating training results from multiple sites to create a global model without directly sharing datasets. This ensures that patient privacy is maintained across sites. Furthermore, the added supervision obtained from the results of partnering sites improves the global model’s overall detection abilities. This alleviates the issue of insufficient supervision when training AI models with small datasets. Lastly, we also address the major challenges of adopting federated learning.},
	number = {2},
	urldate = {2024-01-17},
	journal = {Quantitative Imaging in Medicine and Surgery},
	author = {Ng, Dianwen and Lan, Xiang and Yao, Melissa Min-Szu and Chan, Wing P. and Feng, Mengling},
	month = feb,
	year = {2021},
	pmid = {33532283},
	pmcid = {PMC7779924},
	pages = {852--857},
}

@article{rieke_future_2020,
	title = {The future of digital health with federated learning},
	volume = {3},
	copyright = {2020 The Author(s)},
	issn = {2398-6352},
	url = {https://www.nature.com/articles/s41746-020-00323-1},
	doi = {10.1038/s41746-020-00323-1},
	abstract = {Data-driven machine learning (ML) has emerged as a promising approach for building accurate and robust statistical models from medical data, which is collected in huge volumes by modern healthcare systems. Existing medical data is not fully exploited by ML primarily because it sits in data silos and privacy concerns restrict access to this data. However, without access to sufficient data, ML will be prevented from reaching its full potential and, ultimately, from making the transition from research to clinical practice. This paper considers key factors contributing to this issue, explores how federated learning (FL) may provide a solution for the future of digital health and highlights the challenges and considerations that need to be addressed.},
	language = {en},
	number = {1},
	urldate = {2024-01-17},
	journal = {npj Digital Medicine},
	author = {Rieke, Nicola and Hancox, Jonny and Li, Wenqi and Milletarì, Fausto and Roth, Holger R. and Albarqouni, Shadi and Bakas, Spyridon and Galtier, Mathieu N. and Landman, Bennett A. and Maier-Hein, Klaus and Ourselin, Sébastien and Sheller, Micah and Summers, Ronald M. and Trask, Andrew and Xu, Daguang and Baust, Maximilian and Cardoso, M. Jorge},
	month = sep,
	year = {2020},
	note = {Number: 1
Publisher: Nature Publishing Group},
	keywords = {Medical imaging, Medical research},
	pages = {1--7},
}

@incollection{greenspan_medgen3d_2023,
	address = {Cham},
	title = {{MedGen3D}: {A} {Deep} {Generative} {Framework} for {Paired} {3D} {Image} and {Mask} {Generation}},
	volume = {14220},
	isbn = {978-3-031-43906-3 978-3-031-43907-0},
	shorttitle = {{MedGen3D}},
	url = {https://link.springer.com/10.1007/978-3-031-43907-0_72},
	language = {en},
	urldate = {2023-11-15},
	booktitle = {Medical {Image} {Computing} and {Computer} {Assisted} {Intervention} – {MICCAI} 2023},
	publisher = {Springer Nature Switzerland},
	author = {Han, Kun and Xiong, Yifeng and You, Chenyu and Khosravi, Pooya and Sun, Shanlin and Yan, Xiangyi and Duncan, James S. and Xie, Xiaohui},
	editor = {Greenspan, Hayit and Madabhushi, Anant and Mousavi, Parvin and Salcudean, Septimiu and Duncan, James and Syeda-Mahmood, Tanveer and Taylor, Russell},
	year = {2023},
	doi = {10.1007/978-3-031-43907-0_72},
	note = {Series Title: Lecture Notes in Computer Science},
	pages = {759--769},
}

@article{yi_generative_2019,
	title = {Generative adversarial network in medical imaging: {A} review},
	volume = {58},
	issn = {1361-8415},
	shorttitle = {Generative adversarial network in medical imaging},
	url = {https://www.sciencedirect.com/science/article/pii/S1361841518308430},
	doi = {10.1016/j.media.2019.101552},
	abstract = {Generative adversarial networks have gained a lot of attention in the computer vision community due to their capability of data generation without explicitly modelling the probability density function. The adversarial loss brought by the discriminator provides a clever way of incorporating unlabeled samples into training and imposing higher order consistency. This has proven to be useful in many cases, such as domain adaptation, data augmentation, and image-to-image translation. These properties have attracted researchers in the medical imaging community, and we have seen rapid adoption in many traditional and novel applications, such as image reconstruction, segmentation, detection, classification, and cross-modality synthesis. Based on our observations, this trend will continue and we therefore conducted a review of recent advances in medical imaging using the adversarial training scheme with the hope of benefiting researchers interested in this technique.},
	urldate = {2024-01-16},
	journal = {Medical Image Analysis},
	author = {Yi, Xin and Walia, Ekta and Babyn, Paul},
	month = dec,
	year = {2019},
	keywords = {Deep learning, Generative adversarial network, Generative model, Medical imaging, Review},
	pages = {101552},
}

@article{sun_hierarchical_2022,
	title = {Hierarchical {Amortized} {Training} for {Memory}-efficient {High} {Resolution} {3D} {GAN}},
	volume = {26},
	issn = {2168-2194, 2168-2208},
	url = {http://arxiv.org/abs/2008.01910},
	doi = {10.1109/JBHI.2022.3172976},
	abstract = {Generative Adversarial Networks (GAN) have many potential medical imaging applications, including data augmentation, domain adaptation, and model explanation. Due to the limited memory of Graphical Processing Units (GPUs), most current 3D GAN models are trained on low-resolution medical images, these models either cannot scale to high-resolution or are prone to patchy artifacts. In this work, we propose a novel end-to-end GAN architecture that can generate high-resolution 3D images. We achieve this goal by using different configurations between training and inference. During training, we adopt a hierarchical structure that simultaneously generates a low-resolution version of the image and a randomly selected sub-volume of the high-resolution image. The hierarchical design has two advantages: First, the memory demand for training on high-resolution images is amortized among sub-volumes. Furthermore, anchoring the high-resolution sub-volumes to a single low-resolution image ensures anatomical consistency between sub-volumes. During inference, our model can directly generate full high-resolution images. We also incorporate an encoder with a similar hierarchical structure into the model to extract features from the images. Experiments on 3D thorax CT and brain MRI demonstrate that our approach outperforms state of the art in image generation. We also demonstrate clinical applications of the proposed model in data augmentation and clinical-relevant feature extraction.},
	number = {8},
	urldate = {2024-01-16},
	journal = {IEEE Journal of Biomedical and Health Informatics},
	author = {Sun, Li and Chen, Junxiang and Xu, Yanwu and Gong, Mingming and Yu, Ke and Batmanghelich, Kayhan},
	month = aug,
	year = {2022},
	note = {arXiv:2008.01910 [cs, eess]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing},
	pages = {3966--3975},
}

@misc{bowles_gan_2018,
	title = {{GAN} {Augmentation}: {Augmenting} {Training} {Data} using {Generative} {Adversarial} {Networks}},
	shorttitle = {{GAN} {Augmentation}},
	url = {http://arxiv.org/abs/1810.10863},
	doi = {10.48550/arXiv.1810.10863},
	abstract = {One of the biggest issues facing the use of machine learning in medical imaging is the lack of availability of large, labelled datasets. The annotation of medical images is not only expensive and time consuming but also highly dependent on the availability of expert observers. The limited amount of training data can inhibit the performance of supervised machine learning algorithms which often need very large quantities of data on which to train to avoid overfitting. So far, much effort has been directed at extracting as much information as possible from what data is available. Generative Adversarial Networks (GANs) offer a novel way to unlock additional information from a dataset by generating synthetic samples with the appearance of real images. This paper demonstrates the feasibility of introducing GAN derived synthetic data to the training datasets in two brain segmentation tasks, leading to improvements in Dice Similarity Coefficient (DSC) of between 1 and 5 percentage points under different conditions, with the strongest effects seen fewer than ten training image stacks are available.},
	urldate = {2024-01-16},
	publisher = {arXiv},
	author = {Bowles, Christopher and Chen, Liang and Guerrero, Ricardo and Bentley, Paul and Gunn, Roger and Hammers, Alexander and Dickie, David Alexander and Hernández, Maria Valdés and Wardlaw, Joanna and Rueckert, Daniel},
	month = oct,
	year = {2018},
	note = {arXiv:1810.10863 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
}

@misc{hu_prostategan_2018,
	title = {{ProstateGAN}: {Mitigating} {Data} {Bias} via {Prostate} {Diffusion} {Imaging} {Synthesis} with {Generative} {Adversarial} {Networks}},
	shorttitle = {{ProstateGAN}},
	url = {http://arxiv.org/abs/1811.05817},
	doi = {10.48550/arXiv.1811.05817},
	abstract = {Generative Adversarial Networks (GANs) have shown considerable promise for mitigating the challenge of data scarcity when building machine learning-driven analysis algorithms. Specifically, a number of studies have shown that GAN-based image synthesis for data augmentation can aid in improving classification accuracy in a number of medical image analysis tasks, such as brain and liver image analysis. However, the efficacy of leveraging GANs for tackling prostate cancer analysis has not been previously explored. Motivated by this, in this study we introduce ProstateGAN, a GAN-based model for synthesizing realistic prostate diffusion imaging data. More specifically, in order to generate new diffusion imaging data corresponding to a particular cancer grade (Gleason score), we propose a conditional deep convolutional GAN architecture that takes Gleason scores into consideration during the training process. Experimental results show that high-quality synthetic prostate diffusion imaging data can be generated using the proposed ProstateGAN for specified Gleason scores.},
	urldate = {2024-01-16},
	publisher = {arXiv},
	author = {Hu, Xiaodan and Chung, Audrey G. and Fieguth, Paul and Khalvati, Farzad and Haider, Masoom A. and Wong, Alexander},
	month = nov,
	year = {2018},
	note = {arXiv:1811.05817 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Neural and Evolutionary Computing},
}

@article{dikici_constrained_2021,
	title = {Constrained generative adversarial network ensembles for sharable synthetic medical images},
	volume = {8},
	issn = {2329-4302, 2329-4310},
	url = {https://www.spiedigitallibrary.org/journals/journal-of-medical-imaging/volume-8/issue-2/024004/Constrained-generative-adversarial-network-ensembles-for-sharable-synthetic-medical-images/10.1117/1.JMI.8.2.024004.full},
	doi = {10.1117/1.JMI.8.2.024004},
	abstract = {Purpose: Sharing medical images between institutions, or even inside the same institution, is restricted by various laws and regulations; research projects requiring large datasets may suffer as a result. These limitations might be addressed by an abundant supply of synthetic data that (1) are representative (i.e., the synthetic data could produce comparable research results as the original data) and (2) do not closely resemble the original images (i.e., patient privacy is protected). We introduce a framework that generates data with these requirements leveraging generative adversarial network (GAN) ensembles in a controlled fashion. Approach: To this end, an adaptive ensemble scaling strategy with the objective of representativeness is defined. A sampled Fréchet distance-based constraint was then created to eliminate poorly converged candidates. Finally, a mutual information-based validation metric was embedded into the framework to confirm there are visual differences between the original and the generated synthetic images. Results: The applicability of the solution is demonstrated with a case study for generating three-dimensional brain metastasis (BM) from T1-weighted contrast-enhanced MRI studies. A previously published BM detection system was reported to produce 9.12 false-positives at 90\% detection sensitivity based on the original data. By using the synthetic data generated with the proposed framework, the system produced 9.53 false-positives at the same sensitivity level. Conclusions: Achieving comparable algorithm performance relying solely on synthetic data unveils a significant potential to eliminate/reduce patient privacy concerns when sharing data in medical imaging.},
	number = {2},
	urldate = {2024-01-16},
	journal = {Journal of Medical Imaging},
	author = {Dikici, Engin and Bigelow, Matthew and White, Richard D. and Erdal, Barbaros S. and Prevedello, Luciano M.},
	month = apr,
	year = {2021},
	note = {Publisher: SPIE},
	pages = {024004},
}

@article{chang_mining_2023,
	title = {Mining multi-center heterogeneous medical data with distributed synthetic learning},
	volume = {14},
	copyright = {2023 The Author(s)},
	issn = {2041-1723},
	url = {https://www.nature.com/articles/s41467-023-40687-y},
	doi = {10.1038/s41467-023-40687-y},
	abstract = {Overcoming barriers on the use of multi-center data for medical analytics is challenging due to privacy protection and data heterogeneity in the healthcare system. In this study, we propose the Distributed Synthetic Learning (DSL) architecture to learn across multiple medical centers and ensure the protection of sensitive personal information. DSL enables the building of a homogeneous dataset with entirely synthetic medical images via a form of GAN-based synthetic learning. The proposed DSL architecture has the following key functionalities: multi-modality learning, missing modality completion learning, and continual learning. We systematically evaluate the performance of DSL on different medical applications using cardiac computed tomography angiography (CTA), brain tumor MRI, and histopathology nuclei datasets. Extensive experiments demonstrate the superior performance of DSL as a high-quality synthetic medical image provider by the use of an ideal synthetic quality metric called Dist-FID. We show that DSL can be adapted to heterogeneous data and remarkably outperforms the real misaligned modalities segmentation model by 55\% and the temporal datasets segmentation model by 8\%.},
	language = {en},
	number = {1},
	urldate = {2024-01-16},
	journal = {Nature Communications},
	author = {Chang, Qi and Yan, Zhennan and Zhou, Mu and Qu, Hui and He, Xiaoxiao and Zhang, Han and Baskaran, Lohendran and Al’Aref, Subhi and Li, Hongsheng and Zhang, Shaoting and Metaxas, Dimitris N.},
	month = sep,
	year = {2023},
	note = {Number: 1
Publisher: Nature Publishing Group},
	keywords = {Data acquisition, Data mining, Machine learning, Medical imaging},
	pages = {5510},
}

@article{thambawita_singan-seg_2022,
	title = {{SinGAN}-{Seg}: {Synthetic} training data generation for medical image segmentation},
	volume = {17},
	issn = {1932-6203},
	shorttitle = {{SinGAN}-{Seg}},
	url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0267976},
	doi = {10.1371/journal.pone.0267976},
	abstract = {Analyzing medical data to find abnormalities is a time-consuming and costly task, particularly for rare abnormalities, requiring tremendous efforts from medical experts. Therefore, artificial intelligence has become a popular tool for the automatic processing of medical data, acting as a supportive tool for doctors. However, the machine learning models used to build these tools are highly dependent on the data used to train them. Large amounts of data can be difficult to obtain in medicine due to privacy reasons, expensive and time-consuming annotations, and a general lack of data samples for infrequent lesions. In this study, we present a novel synthetic data generation pipeline, called SinGAN-Seg, to produce synthetic medical images with corresponding masks using a single training image. Our method is different from the traditional generative adversarial networks (GANs) because our model needs only a single image and the corresponding ground truth to train. We also show that the synthetic data generation pipeline can be used to produce alternative artificial segmentation datasets with corresponding ground truth masks when real datasets are not allowed to share. The pipeline is evaluated using qualitative and quantitative comparisons between real data and synthetic data to show that the style transfer technique used in our pipeline significantly improves the quality of the generated data and our method is better than other state-of-the-art GANs to prepare synthetic images when the size of training datasets are limited. By training UNet++ using both real data and the synthetic data generated from the SinGAN-Seg pipeline, we show that the models trained on synthetic data have very close performances to those trained on real data when both datasets have a considerable amount of training data. In contrast, we show that synthetic data generated from the SinGAN-Seg pipeline improves the performance of segmentation models when training datasets do not have a considerable amount of data. All experiments were performed using an open dataset and the code is publicly available on GitHub.},
	language = {en},
	number = {5},
	urldate = {2024-01-16},
	journal = {PLOS ONE},
	author = {Thambawita, Vajira and Salehi, Pegah and Sheshkal, Sajad Amouei and Hicks, Steven A. and Hammer, Hugo L. and Parasa, Sravanthi and Lange, Thomas de and Halvorsen, Pål and Riegler, Michael A.},
	month = may,
	year = {2022},
	note = {Publisher: Public Library of Science},
	keywords = {Algorithms, Artificial intelligence, Computational pipelines, Glioblastoma multiforme, Image analysis, Imaging techniques, Magnetic resonance imaging, Medicine and health sciences},
	pages = {e0267976},
}

@misc{guibas_synthetic_2018,
	title = {Synthetic {Medical} {Images} from {Dual} {Generative} {Adversarial} {Networks}},
	url = {http://arxiv.org/abs/1709.01872},
	doi = {10.48550/arXiv.1709.01872},
	abstract = {Currently there is strong interest in data-driven approaches to medical image classification. However, medical imaging data is scarce, expensive, and fraught with legal concerns regarding patient privacy. Typical consent forms only allow for patient data to be used in medical journals or education, meaning the majority of medical data is inaccessible for general public research. We propose a novel, two-stage pipeline for generating synthetic medical images from a pair of generative adversarial networks, tested in practice on retinal fundi images. We develop a hierarchical generation process to divide the complex image generation task into two parts: geometry and photorealism. We hope researchers will use our pipeline to bring private medical data into the public domain, sparking growth in imaging tasks that have previously relied on the hand-tuning of models. We have begun this initiative through the development of SynthMed, an online repository for synthetic medical images.},
	urldate = {2024-01-16},
	publisher = {arXiv},
	author = {Guibas, John T. and Virdi, Tejpal S. and Li, Peter S.},
	month = jan,
	year = {2018},
	note = {arXiv:1709.01872 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
}

@article{goncalves_generation_2020,
	title = {Generation and evaluation of synthetic patient data},
	volume = {20},
	issn = {1471-2288},
	url = {https://doi.org/10.1186/s12874-020-00977-1},
	doi = {10.1186/s12874-020-00977-1},
	abstract = {Machine learning (ML) has made a significant impact in medicine and cancer research; however, its impact in these areas has been undeniably slower and more limited than in other application domains. A major reason for this has been the lack of availability of patient data to the broader ML research community, in large part due to patient privacy protection concerns. High-quality, realistic, synthetic datasets can be leveraged to accelerate methodological developments in medicine. By and large, medical data is high dimensional and often categorical. These characteristics pose multiple modeling challenges.},
	number = {1},
	urldate = {2024-01-16},
	journal = {BMC Medical Research Methodology},
	author = {Goncalves, Andre and Ray, Priyadip and Soper, Braden and Stevens, Jennifer and Coyle, Linda and Sales, Ana Paula},
	month = may,
	year = {2020},
	keywords = {Cancer patient data, Generative models, Information disclosure, Synthetic data generation},
	pages = {108},
}

@inproceedings{dube_approach_2014,
	address = {Berlin, Heidelberg},
	series = {Lecture {Notes} in {Computer} {Science}},
	title = {Approach and {Method} for {Generating} {Realistic} {Synthetic} {Electronic} {Healthcare} {Records} for {Secondary} {Use}},
	isbn = {978-3-642-53956-5},
	doi = {10.1007/978-3-642-53956-5_6},
	abstract = {This position paper presents research work involving the development of a publicly available Realistic Synthetic Electronic Healthcare Record (RS-EHR). The paper presents PADARSER, a novel approach in which the real Electronic Healthcare Record (EHR) and neither authorization nor anonymisation are required in generating the synthetic EHR data sets. The GRiSER method is presented for use in PADARSER to allow the RS-EHR to be synthesized for statistically significant localised synthetic patients with statistically prevalent medical conditions based upon information found from publicly available data sources. In treating the synthetic patient within the GRiSER method, clinical workflow or careflows (Cfs) are derived from Clinical Practice Guidelines (CPGs) and the standard local practices of clinicians. The Cfs generated are used together with health statistics, CPGs, medical coding and terminology systems to generate coded synthetic RS-EHR entries from statistically significant observations, treatments, tests, and procedures. The RS-EHR is thus populated with a complete medical history describing the resulting events from treating the medical conditions. The strength of the PADARSER approach is its use of publicly available information. The strengths of the GRiSER method are that (1) it does not require the use of the real EHR for generating the coded RS-EHR entries; and (2) the generic components for obtaining careflow from CPGs and for generating coded RS-EHR entries are applicable in other areas such as knowledge transfer and EHR user interfaces respectively.},
	language = {en},
	booktitle = {Foundations of {Health} {Information} {Engineering} and {Systems}},
	publisher = {Springer},
	author = {Dube, Kudakwashe and Gallagher, Thomas},
	editor = {Gibbons, Jeremy and MacCaull, Wendy},
	year = {2014},
	keywords = {ICD10, SNOMED-CT, clinical guidelines, clinical workflow, electronic health record, healthcare statistics, knowledge modeling, medical terminology, synthetic data},
	pages = {69--86},
}

@article{isensee_automated_2021,
	title = {Automated {Design} of {Deep} {Learning} {Methods} for {Biomedical} {Image} {Segmentation}},
	volume = {18},
	issn = {1548-7091, 1548-7105},
	url = {http://arxiv.org/abs/1904.08128},
	doi = {10.1038/s41592-020-01008-z},
	abstract = {Biomedical imaging is a driver of scientific discovery and core component of medical care, currently stimulated by the field of deep learning. While semantic segmentation algorithms enable 3D image analysis and quantification in many applications, the design of respective specialised solutions is non-trivial and highly dependent on dataset properties and hardware conditions. We propose nnU-Net, a deep learning framework that condenses the current domain knowledge and autonomously takes the key decisions required to transfer a basic architecture to different datasets and segmentation tasks. Without manual tuning, nnU-Net surpasses most specialised deep learning pipelines in 19 public international competitions and sets a new state of the art in the majority of the 49 tasks. The results demonstrate a vast hidden potential in the systematic adaptation of deep learning methods to different datasets. We make nnU-Net publicly available as an open-source tool that can effectively be used out-of-the-box, rendering state of the art segmentation accessible to non-experts and catalyzing scientific progress as a framework for automated method design.},
	number = {2},
	urldate = {2023-10-11},
	journal = {Nature Methods},
	author = {Isensee, Fabian and Jäger, Paul F. and Kohl, Simon A. A. and Petersen, Jens and Maier-Hein, Klaus H.},
	month = feb,
	year = {2021},
	note = {arXiv:1904.08128 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
	pages = {203--211},
}
