Synthetic dataset for A Deep Learning Approach to Private Data Sharing of Medical Images Using Conditional GANs

Dataset specification:

• MRI images of Vertebral Units labelled based on region
• Dataset is comprised of 10000 pairs of images and labels
• Image and label pair number k can be selected by: synthetic_dataset['images'][k] and synthetic_dataset['regions'][k]
• Images are 3D of size (9, 64, 64)
• Regions are stored as an integer. Mapping is 0: cervical, 1: thoracic, 2: lumbar

Arxiv paper: https://arxiv.org/abs/2106.13199
Github code: https://github.com/tcoroller/pGAN/

Abstract:

Sharing data from clinical studies can facilitate innovative data-driven research and ultimately lead to better public health. However, sharing biomedical data can put sensitive personal information at risk. This is usually solved by anonymization, which is a slow and expensive process. An alternative to anonymization is sharing a synthetic dataset that bears a behaviour similar to the real data but preserves privacy. As part of the collaboration between Novartis and the Oxford Big Data Institute, we generate a synthetic dataset based on COSENTYX Ankylosing Spondylitis (AS) clinical study. We apply an Auxiliary Classifier GAN (ac-GAN) to generate synthetic magnetic resonance images (MRIs) of vertebral units (VUs). The images are conditioned on the VU location (cervical, thoracic and lumbar). In this paper, we present a method for generating a synthetic dataset and conduct an in-depth analysis on its properties of along three key metrics: image fidelity, sample diversity and dataset privacy.

Generative adversarial networks (GANs) have recently been successfully used to create realistic synthetic microscopy cell images in 2D and predict intermediate cell stages. In the current paper we highlight that GANs can not only be used for creating synthetic cell images optimized for different fluorescent molecular labels, but that by using GANs for augmentation of training data involving scaling or other transformations the inherent length scale of biological structures is retained. In addition, GANs make it possible to create synthetic cells with specific shape features, which can be used, for example, to validate different methods for feature extraction. Here, we apply GANs to create 2D distributions of fluorescent markers for F-actin in the cell cortex of Dictyostelium cells (ABD), a membrane receptor (cAR1), and a cortex-membrane linker protein (TalA). The recent more widespread use of 3D lightsheet microscopy, where obtaining sufficient training data is considerably more difficult than in 2D, creates significant demand for novel approaches to data augmentation. We show that it is possible to directly generate synthetic 3D cell images using GANs, but limitations are excessive training times, dependence on high-quality segmentations of 3D images, and that the number of z-slices cannot be freely adjusted without retraining the network. We demonstrate that in the case of molecular labels that are highly correlated with cell shape, like F-actin in our example, 2D GANs can be used efficiently to create pseudo-3D synthetic cell data from individually generated 2D slices. Because high quality segmented 2D cell data are more readily available, this is an attractive alternative to using less efficient 3D networks.

Synthetic Data Video Generator Market Outlook

According to our latest research, the global Synthetic Data Video Generator market size in 2024 stands at USD 1.46 billion, with robust momentum driven by advances in artificial intelligence and the increasing need for high-quality, privacy-compliant video datasets. The market is witnessing a remarkable compound annual growth rate (CAGR) of 37.2% from 2025 to 2033, propelled by growing adoption across sectors such as autonomous vehicles, healthcare, and surveillance. By 2033, the market is projected to reach USD 18.16 billion, reflecting a seismic shift in how organizations leverage synthetic data to accelerate innovation and mitigate data privacy concerns.

The primary growth factor for the Synthetic Data Video Generator market is the surging demand for data privacy and compliance in machine learning and computer vision applications. As regulatory frameworks like GDPR and CCPA become more stringent, organizations are increasingly wary of using real-world video data that may contain personally identifiable information. Synthetic data video generators provide a scalable and ethical alternative, enabling enterprises to train and validate AI models without risking privacy breaches. This trend is particularly pronounced in sectors such as healthcare and finance, where data sensitivity is paramount. The ability to generate diverse, customizable, and annotation-rich video datasets not only addresses compliance requirements but also accelerates the development and deployment of AI solutions.

Another significant driver is the rapid evolution of deep learning algorithms and simulation technologies, which have dramatically improved the realism and utility of synthetic video data. Innovations in generative adversarial networks (GANs), 3D rendering engines, and advanced simulation platforms have made it possible to create synthetic videos that closely mimic real-world environments and scenarios. This capability is invaluable for industries like autonomous vehicles and robotics, where extensive and varied training data is essential for safe and reliable system behavior. The reduction in time, cost, and logistical complexity associated with collecting and labeling real-world video data further enhances the attractiveness of synthetic data video generators, positioning them as a cornerstone technology for next-generation AI development.

The expanding use cases for synthetic video data across emerging applications also contribute to market growth. Beyond traditional domains such as surveillance and entertainment, synthetic data video generators are finding adoption in areas like augmented reality, smart retail, and advanced robotics. The flexibility to simulate rare, dangerous, or hard-to-capture scenarios offers a strategic advantage for organizations seeking to future-proof their AI initiatives. As synthetic data generation platforms become more accessible and user-friendly, small and medium enterprises are also entering the fray, democratizing access to high-quality training data and fueling a new wave of AI-driven innovation.

From a regional perspective, North America continues to dominate the Synthetic Data Video Generator market, benefiting from a concentration of technology giants, research institutions, and early adopters across key verticals. Europe follows closely, driven by strong regulatory emphasis on data protection and an active ecosystem of AI startups. Meanwhile, the Asia Pacific region is emerging as a high-growth market, buoyed by rapid digital transformation, government AI initiatives, and increasing investments in autonomous systems and smart cities. Latin America and the Middle East & Africa are also showing steady progress, albeit from a smaller base, as awareness and infrastructure for synthetic data generation mature.

Component Analysis

The Synthetic Data Video Generator market, when analyzed by component, is primarily segmented into Software and Services. The software segment currently commands the largest share, driven by the prolif

including both sunny and cloudy days.

Synthetic Data Video Generator Market Outlook

According to our latest research, the global synthetic data video generator market size reached USD 1.32 billion in 2024 and is anticipated to grow at a robust CAGR of 38.7% from 2025 to 2033. By the end of 2033, the market is projected to reach USD 18.59 billion, driven by rapid advancements in artificial intelligence, the growing need for high-quality training data for machine learning models, and increasing adoption across industries such as autonomous vehicles, healthcare, and surveillance. The surge in demand for data privacy, coupled with the necessity to overcome data scarcity and bias in real-world datasets, is significantly fueling the synthetic data video generator market's growth trajectory.

One of the primary growth factors for the synthetic data video generator market is the escalating demand for high-fidelity, annotated video datasets required to train and validate AI-driven systems. Traditional data collection methods are often hampered by privacy concerns, high costs, and the sheer complexity of obtaining diverse and representative video samples. Synthetic data video generators address these challenges by enabling the creation of large-scale, customizable, and bias-free datasets that closely mimic real-world scenarios. This capability is particularly vital for sectors such as autonomous vehicles and robotics, where the accuracy and safety of AI models depend heavily on the quality and variety of training data. As organizations strive to accelerate innovation and reduce the risks associated with real-world data collection, the adoption of synthetic data video generation technologies is expected to expand rapidly.

Another significant driver for the synthetic data video generator market is the increasing regulatory scrutiny surrounding data privacy and compliance. With stricter regulations such as GDPR and CCPA coming into force, organizations face mounting challenges in using real-world video data that may contain personally identifiable information. Synthetic data offers an effective solution by generating video datasets devoid of any real individuals, thereby ensuring compliance while still enabling advanced analytics and machine learning. Moreover, synthetic data video generators empower businesses to simulate rare or hazardous events that are difficult or unethical to capture in real life, further enhancing model robustness and preparedness. This advantage is particularly pronounced in healthcare, surveillance, and automotive industries, where data privacy and safety are paramount.

Technological advancements and increasing integration with cloud-based platforms are also propelling the synthetic data video generator market forward. The proliferation of cloud computing has made it easier for organizations of all sizes to access scalable synthetic data generation tools without significant upfront investments in hardware or infrastructure. Furthermore, the continuous evolution of generative adversarial networks (GANs) and other deep learning techniques has dramatically improved the realism and utility of synthetic video data. As a result, companies are now able to generate highly realistic, scenario-specific video datasets at scale, reducing both the time and cost required for AI development. This democratization of synthetic data technology is expected to unlock new opportunities across a wide array of applications, from entertainment content production to advanced surveillance systems.

From a regional perspective, North America currently dominates the synthetic data video generator market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The strong presence of leading AI technology providers, robust investment in research and development, and early adoption by automotive and healthcare sectors are key contributors to North America's market leadership. Europe is also witnessing significant growth, driven by stringent data privacy regulations and increased focus on AI-driven innovation. Meanwhile, Asia Pacific is emerging as a high-growth region, fueled by rapid digital transformation, expanding IT infrastructure, and increasing investments in autonomous systems and smart city projects. Latin America and Middle East & Africa, while still nascent, are expected to experience steady uptake as awareness and technological capabilities continue to grow.

Component Analysis

The synthetic data video generator market by comp

The Synthetic Data Platform market is experiencing robust growth, driven by the increasing need for data privacy and security, coupled with the rising demand for AI and machine learning model training. The market's expansion is fueled by several key factors. Firstly, stringent data privacy regulations like GDPR and CCPA are limiting the use of real-world data, creating a surge in demand for synthetic data that mimics the characteristics of real data without compromising sensitive information. Secondly, the expanding applications of AI and ML across diverse sectors like healthcare, finance, and transportation require massive datasets for effective model training. Synthetic data provides a scalable and cost-effective solution to this challenge, enabling organizations to build and test models without the limitations imposed by real data scarcity or privacy concerns. Finally, advancements in synthetic data generation techniques, including generative adversarial networks (GANs) and variational autoencoders (VAEs), are continuously improving the quality and realism of synthetic datasets, making them increasingly viable alternatives to real data. The market is segmented by application (Government, Retail & eCommerce, Healthcare & Life Sciences, BFSI, Transportation & Logistics, Telecom & IT, Manufacturing, Others) and type (Cloud-Based, On-Premises). While the cloud-based segment currently dominates due to its scalability and accessibility, the on-premises segment is expected to witness growth driven by organizations prioritizing data security and control. Geographically, North America and Europe are currently leading the market, owing to the presence of mature technological infrastructure and a high adoption rate of AI and ML technologies. However, Asia-Pacific is anticipated to show significant growth potential in the coming years, driven by increasing digitalization and investments in AI across the region. While challenges remain in terms of ensuring the quality and fidelity of synthetic data and addressing potential biases in generated datasets, the overall outlook for the Synthetic Data Platform market remains highly positive, with substantial growth projected over the forecast period. We estimate a CAGR of 25% from 2025 to 2033.

Synthetic Data Generation Engine Market Outlook

According to our latest research, the global synthetic data generation engine market size reached USD 1.48 billion in 2024. The market is experiencing robust expansion, driven by the increasing demand for privacy-compliant data and advanced analytics solutions. The market is projected to grow at a remarkable CAGR of 35.6% from 2025 to 2033, reaching an estimated USD 18.67 billion by the end of the forecast period. This rapid growth is primarily propelled by the adoption of artificial intelligence (AI) and machine learning (ML) across various industry verticals, along with the escalating need for high-quality, diverse datasets that do not compromise sensitive information.

One of the primary growth factors fueling the synthetic data generation engine market is the heightened focus on data privacy and regulatory compliance. With stringent regulations such as GDPR, CCPA, and HIPAA being enforced globally, organizations are increasingly seeking solutions that enable them to generate and utilize data without exposing real customer information. Synthetic data generation engines provide a powerful means to create realistic, anonymized datasets that retain the statistical properties of original data, thus supporting robust analytics and model development while ensuring compliance with data protection laws. This capability is especially critical for sectors like healthcare, banking, and government, where data sensitivity is paramount.

Another significant driver is the surging adoption of AI and ML models across industries, which require vast volumes of diverse and representative data for training and validation. Traditional data collection methods often fall short due to limitations in data availability, quality, or privacy concerns. Synthetic data generation engines address these challenges by enabling the creation of customized datasets tailored for specific use cases, including rare-event modeling, edge-case scenario testing, and data augmentation. This not only accelerates innovation but also reduces the time and cost associated with data acquisition and labeling, making it a strategic asset for organizations seeking to maintain a competitive edge in AI-driven markets.

Moreover, the increasing integration of synthetic data generation engines into enterprise IT ecosystems is being catalyzed by advancements in cloud computing and scalable software architectures. Cloud-based deployment models are making these solutions more accessible and cost-effective for organizations of all sizes, from startups to large enterprises. The flexibility to generate, store, and manage synthetic datasets in the cloud enhances collaboration, speeds up development cycles, and supports global operations. As a result, cloud adoption is expected to further accelerate market growth, particularly among businesses undergoing digital transformation and seeking to leverage synthetic data for innovation and compliance.

Regionally, North America currently dominates the synthetic data generation engine market, accounting for the largest revenue share in 2024, followed closely by Europe and the Asia Pacific. North America's leadership is attributed to the presence of major technology providers, robust regulatory frameworks, and a high level of AI adoption across industries. Europe is experiencing rapid growth due to strong data privacy regulations and a thriving technology ecosystem, while Asia Pacific is emerging as a lucrative market, driven by digitalization initiatives and increasing investments in AI and analytics. The regional outlook suggests that market expansion will be broad-based, with significant opportunities for vendors and stakeholders across all major geographies.

Component Analysis

The component segment of the synthetic data generation engine market is bifurcated into software and services, each playing a vital role in the overall ecosystem. Software solutions form the backbone of this market, providing the core algorithms and platforms that enable the generation, management, and deployment of synthetic datasets. These platforms are continually evolving, integrating advanced techniques such as generative adversarial networks (GANs), variational autoencoders, and other deep learning models to produce highly realistic and diverse synthetic data. The software segment is anticipated to maintain its dominance throughout the forecast period, as organizations increasingly invest in proprietary and commercial tools to address their un

FID value comparison between the real dataset of 1000 real images and the synthetic datasets of 1000 synthetic images generated from different GAN architectures which are modified to generate four channels outputs.

AI-Generated Synthetic Tabular Dataset Market Outlook

According to our latest research, the AI-Generated Synthetic Tabular Dataset market size reached USD 1.12 billion globally in 2024, with a robust CAGR of 34.7% expected during the forecast period. By 2033, the market is forecasted to reach an impressive USD 15.32 billion. This remarkable growth is primarily attributed to the increasing demand for privacy-preserving data solutions, the surge in AI-driven analytics, and the critical need for high-quality, diverse datasets across industries. The proliferation of regulations around data privacy and the rapid digital transformation of sectors such as healthcare, finance, and retail are further fueling market expansion as organizations seek innovative ways to leverage data without compromising compliance or security.

One of the key growth factors for the AI-Generated Synthetic Tabular Dataset market is the escalating importance of data privacy and compliance with global regulations such as GDPR, HIPAA, and CCPA. As organizations collect and process vast amounts of sensitive information, the risk of data breaches and misuse grows. Synthetic tabular datasets, generated using advanced AI algorithms, offer a viable solution by mimicking real-world data patterns without exposing actual personal or confidential information. This not only ensures regulatory compliance but also enables organizations to continue their data-driven innovation, analytics, and AI model training without legal or ethical hindrances. The ability to generate high-fidelity, statistically accurate synthetic data is transforming data governance strategies across industries.

Another significant driver is the exponential growth of AI and machine learning applications that demand large, diverse, and high-quality datasets. In many cases, access to real data is limited due to privacy, security, or proprietary concerns. AI-generated synthetic tabular datasets bridge this gap by providing scalable, customizable data that closely mirrors real-world scenarios. This accelerates the development and deployment of AI models in sectors like healthcare, where patient data is highly sensitive, or in finance, where transaction records are strictly regulated. The synthetic data market is also benefiting from advancements in generative AI techniques, such as GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders), which have significantly improved the realism and utility of synthetic tabular data.

A third major growth factor is the increasing adoption of cloud computing and the integration of synthetic data generation tools into enterprise data pipelines. Cloud-based synthetic data platforms offer scalability, flexibility, and ease of integration with existing data management and analytics systems. Enterprises are leveraging these platforms to enhance data availability for testing, training, and validation of AI models, particularly in environments where access to production data is restricted. The shift towards cloud-native architectures is also enabling real-time synthetic data generation and consumption, further driving the adoption of AI-generated synthetic tabular datasets across various business functions.

From a regional perspective, North America currently dominates the AI-Generated Synthetic Tabular Dataset market, accounting for the largest share in 2024. This leadership is driven by the presence of major technology companies, strong investments in AI research, and stringent data privacy regulations. Europe follows closely, with significant growth fueled by the enforcement of GDPR and increasing awareness of data privacy solutions. The Asia Pacific region is emerging as a high-growth market, propelled by rapid digitalization, expanding AI ecosystems, and government initiatives promoting data innovation. Latin America and the Middle East & Africa are also witnessing steady adoption, albeit at a slower pace, as organizations in these regions recognize the value of synthetic data in overcoming data access and privacy challenges.

Component Analysis

The AI-Generated Synthetic Tabular Dataset market by component is segmented into software and services, with each playing a pivotal role in shaping the industry landscape. Software solutions comprise platforms and tools that automate the generation of synthetic tabular data using advanced AI algorithms. These platforms are increasingly being adopted by enterprises seeking

GAN-Synthesized Augmented Radiology Dataset Market Outlook

According to our latest research, the GAN-Synthesized Augmented Radiology Dataset market size reached USD 412 million in 2024, supported by a robust surge in the adoption of artificial intelligence across healthcare imaging. The market demonstrated a strong CAGR of 25.7% from 2021 to 2024 and is on track to reach a valuation of USD 3.2 billion by 2033. The primary growth factor fueling this expansion is the increasing demand for high-quality, diverse, and annotated radiology datasets to train and validate advanced AI diagnostic models, especially as regulatory requirements for clinical validation intensify globally.

The exponential growth of the GAN-Synthesized Augmented Radiology Dataset market is being driven by the urgent need for large-scale, diverse, and unbiased datasets in medical imaging. Traditional methods of acquiring and annotating radiological images are time-consuming, expensive, and often limited by patient privacy concerns. Generative Adversarial Networks (GANs) have emerged as a transformative technology, enabling the synthesis of high-fidelity, realistic medical images that can augment existing datasets. This not only enhances the statistical power and generalizability of AI models but also helps overcome the challenge of data imbalance, especially for rare diseases and underrepresented demographic groups. As AI-driven diagnostics become integral to clinical workflows, the reliance on GAN-augmented datasets is expected to intensify, further propelling market growth.

Another significant growth driver is the increasing collaboration between radiology departments, AI technology vendors, and academic research institutes. These partnerships are focused on developing standardized protocols for dataset generation, annotation, and validation, leveraging GANs to create synthetic images that closely mimic real-world clinical scenarios. The resulting datasets facilitate the training of AI algorithms for a wide array of applications, including disease detection, anomaly identification, and image segmentation. Additionally, the proliferation of cloud-based platforms and open-source AI frameworks has democratized access to GAN-synthesized datasets, enabling even smaller healthcare organizations and startups to participate in the AI-driven transformation of radiology.

The regulatory landscape is also evolving to support the responsible use of synthetic data in healthcare. Regulatory agencies in North America, Europe, and Asia Pacific are increasingly recognizing the value of GAN-generated datasets for algorithm validation, provided they meet stringent standards for data quality, privacy, and clinical relevance. This regulatory endorsement is encouraging more hospitals, diagnostic centers, and research institutions to adopt GAN-augmented datasets, further accelerating market expansion. Moreover, the ongoing advancements in GAN architectures, such as StyleGAN and CycleGAN, are enhancing the realism and diversity of synthesized images, making them virtually indistinguishable from real patient scans and boosting their acceptance in both clinical and research settings.

From a regional perspective, North America is currently the largest market for GAN-Synthesized Augmented Radiology Datasets, driven by substantial investments in healthcare AI, the presence of leading technology vendors, and proactive regulatory support. Europe follows closely, with a strong emphasis on data privacy and cross-border research collaborations. The Asia Pacific region is witnessing the fastest growth, fueled by rapid digital transformation in healthcare, rising investments in AI infrastructure, and increasing disease burden. Latin America and the Middle East & Africa are also emerging as promising markets, albeit at a slower pace, as healthcare systems in these regions begin to adopt AI-driven radiology solutions.

Dataset Type Analysis

The dataset type segment of the GAN-Synthesized Augmented Radiology Dataset market is pi

TrueFace is a first dataset of social media processed real and synthetic faces, obtained by the successful StyleGAN generative models, and shared on Facebook, Twitter and Telegram.

Images have historically been a universal and cross-cultural communication medium, capable of reaching people of any social background, status or education. Unsurprisingly though, their social impact has often been exploited for malicious purposes, like spreading misinformation and manipulating public opinion. With today's technologies, the possibility to generate highly realistic fakes is within everyone's reach. A major threat derives in particular from the use of synthetically generated faces, which are able to deceive even the most experienced observer. To contrast this fake news phenomenon, researchers have employed artificial intelligence to detect synthetic images by analysing patterns and artifacts introduced by the generative models. However, most online images are subject to repeated sharing operations by social media platforms. Said platforms process uploaded images by applying operations (like compression) that progressively degrade those useful forensic traces, compromising the effectiveness of the developed detectors. To solve the synthetic-vs-real problem "in the wild", more realistic image databases, like TrueFace, are needed to train specialised detectors.

a good solution is to augment the DBs with appropriate techniques

Most facial expression recognition (FER) systems rely on machine learning approaches that require large databases (DBs) for effective training. As these are not easily available, a good solution is to augment the DBs with appropriate techniques, which are typically based on either geometric transformation or deep learning based technologies (e.g., Generative Adversarial Networks (GANs)). Whereas the first category of techniques has been fairly adopted in the past, studies that use GAN-based techniques are limited for FER systems. To advance in this respect, we evaluate the impact of the GAN techniques by creating a new DB containing the generated synthetic images.

The face images contained in the KDEF DB serve as the basis for creating novel synthetic images by combining the facial features of two images (i.e., Candie Kung and Cristina Saralegui) selected from the YouTube-Faces DB. The novel images differ from each other, in particular concerning the eyes, the nose, and the mouth, whose characteristics are taken from the Candie and Cristina images.

The total number of novel synthetic images generated with the GAN is 980 (70 individuals from KDEF DB x 7 emotions x 2 subjects from YouTube-Faces DB).

The zip file "GAN_KDEF_Candie" contains the 490 images generated by combining the KDEF images with the Candie Kung image. The zip file "GAN_KDEF_Cristina" contains the 490 images generated by combining the KDEF images with the Cristina Saralegui image. The used image IDs are the same used for the KDEF DB. The synthetic generated images have a resolution of 562x762 pixels.

If you make use of this dataset, please consider citing the following publication:

Porcu, S., Floris, A., & Atzori, L. (2020). Evaluation of Data Augmentation Techniques for Facial Expression Recognition Systems. Electronics, 9, 1892, doi: 10.3390/electronics9111892, url: https://www.mdpi.com/2079-9292/9/11/1892.

BibTex format:

@article{porcu2020evaluation, title={Evaluation of Data Augmentation Techniques for Facial Expression Recognition Systems}, author={Porcu, Simone and Floris, Alessandro and Atzori, Luigi}, journal={Electronics}, volume={9}, pages={108781}, year={2020}, number = {11}, article-number = {1892}, publisher={MDPI}, doi={10.3390/electronics9111892} }

Diabetic retinopathy (DR) is a prominent reason of blindness globally, which is a diagnostically challenging disease owing to the intricate process of its development and the human eye’s complexity, which consists of nearly forty connected components like the retina, iris, optic nerve, and so on. This study proposes a novel approach to the identification of DR employing methods such as synthetic data generation, K- Means Clustering-Based Binary Grey Wolf Optimizer (KCBGWO), and Fully Convolutional Encoder-Decoder Networks (FCEDN). This is achieved using Generative Adversarial Networks (GANs) to generate high-quality synthetic data and transfer learning for accurate feature extraction and classification, integrating these with Extreme Learning Machines (ELM). The substantial evaluation plan we have provided on the IDRiD dataset gives exceptional outcomes, where our proposed model gives 99.87% accuracy and 99.33% sensitivity, while its specificity is 99. 78%. This is why the outcomes of the presented study can be viewed as promising in terms of the further development of the proposed approach for DR diagnosis, as well as in creating a new reference point within the framework of medical image analysis and providing more effective and timely treatments.

Synthetic Tabular Data Market Outlook

According to our latest research, the global synthetic tabular data market size in 2024 stands at USD 470 million, reflecting a robust demand across multiple sectors driven by the need for privacy-preserving data and advanced analytics. The market is projected to grow at a CAGR of 35.8% from 2025 to 2033, reaching a forecasted value of USD 6.9 billion by 2033. Key growth factors include the increasing adoption of artificial intelligence and machine learning, stringent data privacy regulations worldwide, and the growing necessity for high-quality, diverse datasets to fuel innovation while minimizing compliance risks.

One of the primary growth drivers in the synthetic tabular data market is the escalating emphasis on data privacy and compliance with global regulations such as GDPR, CCPA, and HIPAA. Organizations are under immense pressure to safeguard sensitive information while still leveraging data for insights and competitive advantage. Synthetic tabular data, which mimics real datasets without exposing actual personal or confidential information, offers a compelling solution. This technology enables businesses to conduct analytics, develop machine learning models, and perform robust testing without risking data breaches or non-compliance penalties. The rising number of data privacy incidents and the growing public scrutiny over data handling practices have further accelerated the adoption of synthetic data solutions across industries.

Another significant factor fueling market expansion is the exponential growth in artificial intelligence and machine learning initiatives across various sectors. Machine learning algorithms require vast, diverse, and high-quality datasets to train and validate models effectively. However, access to such data is often restricted due to privacy concerns, data scarcity, or regulatory barriers. Synthetic tabular data addresses this challenge by generating realistic, statistically representative datasets that closely resemble actual data distributions. This fosters innovation in areas such as fraud detection, predictive analytics, and recommendation systems, empowering organizations to build more accurate and robust AI models while maintaining data confidentiality.

Additionally, the synthetic tabular data market is benefiting from advancements in generative modeling techniques, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). These technologies have significantly improved the fidelity and utility of synthetic data, making it increasingly difficult to distinguish from real-world datasets. As a result, industries like healthcare, finance, and retail are embracing synthetic tabular data for applications ranging from clinical research and financial risk modeling to customer behavior analysis and supply chain optimization. The growing ecosystem of synthetic data platforms, tools, and services is also lowering the barriers to entry, enabling organizations of all sizes to harness the benefits of synthetic data.

From a regional perspective, North America currently leads the synthetic tabular data market, driven by a mature technology landscape, early adoption of AI and data privacy frameworks, and significant investments in research and development. Europe follows closely, propelled by stringent GDPR regulations and a strong focus on ethical AI. The Asia Pacific region is emerging as a high-growth market, supported by rapid digital transformation, expanding data-driven industries, and increasing awareness of data privacy issues. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as enterprises in these regions recognize the value of synthetic data for digital innovation and regulatory compliance.

Data Type Analysis

The synthetic tabular data market is segmented by data type into numerical, categorical, and mixed datasets, each serving distinct use cases and industries. Numerical synthetic data, representing quantitative values such as sales figures, sensor readings, or financial metrics, is particularly vital for sectors that rely heavily on statistical analysis and predictive modeling. Organizations in finance, manufacturing, and scientific research utilize numerical synthetic data to simulate scenarios, perform stress testing, and enhance the robustness of their analytical models. The ability to generate large volumes of realistic numer

Dataset Title

Skin Disease GAN-Generated and Original Images Lightweight Dataset

General Description

This dataset is a collection of skin disease images generated using a Generative Adversarial Network (GAN) approach. Specifically, a GAN was utilized with Stable Diffusion as the generator and a transformer-based discriminator to create realistic images of various skin diseases. The GAN approach enhances the accuracy and realism of the generated images, making this dataset a valuable resource for machine learning and computer vision applications in dermatology.

Creation Process

To create this dataset, a series of Low-Rank Adaptations (LoRAs) were generated for each disease category. These LoRAs were trained on the base dataset with 60 epochs and 30,000 steps using OneTrainer. Images were then generated for the following disease categories:

• Herpes
• Measles
• Chickenpox
• Monkeypox

Due to the availability of ample public images, Melanoma was excluded from the generation process. The Fooocus API served as the generator within the GAN framework, creating images based on the LoRAs.

To ensure quality and accuracy, a transformer-based discriminator was employed to verify the generated images, classifying them into the correct disease categories.

Sources

The original base dataset used to create this GAN-based dataset includes reputable sources such as:

2019 HAM10000 Challenge - Kaggle - Google Images - Dermnet NZ - Bing Images - Yandex - Hellenic Atlas - Dermatological Atlas The LoRAs and their recommended weights for generating images are available for download on our CivitAi profile. You can refer to this profile for detailed instructions and access to the LoRAs used in this dataset.

Dataset Contents

Generated Images: High-quality images of skin diseases generated via GAN with Stable Diffusion, using transformer-based discrimination for accurate classification.

Categories

• Herpes
• Measles
• Chickenpox
• Monkeypox Each image corresponds to one of these four categories, providing a reliable set of generated data for training and evaluation. Melanoma was excluded from generation due to the abundance of public data.

Suggested Use Cases

This dataset is suitable for:

• Image Classification and Augmentation Tasks: Training and evaluating models in skin disease classification, with additional augmentation from generated images.
• Research in Dermatology and GAN Techniques: Investigating the effectiveness of GANs for generating medical images, as well as exploring the use of transformer-based discrimination.
• Educational Projects in AI and Medicine: Offering insights into image generation for diagnostic purposes, combining GANs and Stable Diffusion with transformers for medical datasets.

Citation

Garcia-Espinosa, E. ., Ruiz-Castilla, J. S., & Garcia-Lamont, F. (2025). Generative AI and Transformers in Advanced Skin Lesion Classification applied on a mobile device. International Journal of Combinatorial Optimization Problems and Informatics, 16(2), 158–175. https://doi.org/10.61467/2007.1558.2025.v16i2.1078

** **

Espinosa, E.G., Castilla, J.S.R., Lamont, F.G. (2025). Skin Disease Pre-diagnosis with Novel Visual Transformers. In: Figueroa-García, J.C., Hernández, G., Suero Pérez, D.F., Gaona García, E.E. (eds) Applied Computer Sciences in Engineering. WEA 2024. Communications in Computer and Information Science, vol 2222. Springer, Cham. https://doi.org/10.1007/978-3-031-74595-9_10

The AIGC (AI-Generated Content) market for algorithmic models and datasets is experiencing rapid growth, driven by increasing demand for AI-powered solutions across various sectors. The market, while currently estimated at approximately $5 billion in 2025, is projected to expand significantly, exhibiting a robust Compound Annual Growth Rate (CAGR) of 35% from 2025 to 2033. This growth is fueled by several key factors: the proliferation of large language models (LLMs), advancements in deep learning techniques enabling more sophisticated model generation, and the increasing availability of high-quality training datasets. Companies like Meta, Baidu, and several Chinese technology firms are heavily invested in this space, competing to develop and deploy cutting-edge AIGC technologies. The market is segmented by model type (e.g., generative adversarial networks (GANs), transformers), dataset type (e.g., image, text, video), and application (e.g., natural language processing (NLP), computer vision). While data security and ethical concerns pose potential restraints, the overall market outlook remains extremely positive, driven by the relentless innovation in artificial intelligence. Further fueling this expansion is the increasing adoption of AIGC in diverse industries. Businesses are leveraging AIGC to automate content creation, personalize user experiences, and gain valuable insights from complex data sets. The ability of AIGC to generate synthetic data for training and testing purposes is also proving invaluable, particularly in scenarios where real-world data is scarce or expensive to acquire. The competitive landscape is dynamic, with both established tech giants and emerging startups vying for market share. Geographic distribution is likely skewed towards regions with advanced technological infrastructure and strong AI research capabilities, including North America, Europe, and East Asia. While regulatory hurdles and potential biases in AI-generated content require careful attention, the long-term growth trajectory for this segment of the AIGC market remains exceptionally strong, promising substantial economic and technological advancements.

Background: The utilization of artificial intelligence (AI) in healthcare has significant potential to revolutionize the delivery of medical services, particularly in the field of telemedicine. In this article, we investigate the capabilities of a specific deep learning model, a Generative Adversarial Network (GAN), and explore its potential for enhancing the telemedicine approach to cancer pain management.

Materials and Methods: We implemented a structured dataset comprising demographic and clinical variables from 226 patients and 489 telemedicine visits for cancer pain management. The deep learning model, specifically a conditional GAN, was employed to generate synthetic samples from our dataset that closely resemble real individuals in terms of their characteristics. Subsequently, four machine learning algorithms were implemented to assess the variables associated with a higher number of remote visits.

Results: The generated dataset exhibits a distribution comparable to the reference dataset for all considered variables, including age, number of visits, tumor type, performance status, characteristics of metastasis, opioid dosage, and type of pain. Among the algorithms tested, Random Forest demonstrated the highest performance in predicting a higher number of remote visits, achieving an accuracy of 0.8 on the test data.

Conclusion: As the advancement of healthcare processes relies on scientific evidence, AI techniques such as GANs can play a vital role in bridging knowledge gaps and accelerating the integration of telemedicine into clinical practice. Nonetheless, it is crucial to carefully address the limitations associated with these approaches.

Synthetic Health Data Market Outlook

According to our latest research, the global synthetic health data market size reached USD 312.4 million in 2024. The market is demonstrating robust momentum, growing at a CAGR of 31.2% from 2025 to 2033. By 2033, the synthetic health data market is forecasted to achieve a value of USD 3.14 billion. This remarkable growth is primarily driven by the increasing demand for privacy-compliant, high-quality datasets to accelerate innovation across healthcare research, clinical trials, and digital health solutions.

One of the most significant growth drivers for the synthetic health data market is the intensifying focus on data privacy and regulatory compliance. Healthcare organizations are under mounting pressure to adhere to stringent regulations such as HIPAA in the United States and GDPR in Europe. These frameworks restrict the sharing and utilization of real patient data, creating a critical need for synthetic health data that mimics real-world datasets without compromising patient privacy. The ability of synthetic data to facilitate research, AI training, and analytics without the risk of identifying individuals is a key factor fueling its widespread adoption among healthcare providers, pharmaceutical companies, and research organizations globally.

Technological advancements in artificial intelligence and machine learning are further propelling the synthetic health data market forward. The sophistication of generative models, such as GANs and variational autoencoders, has enabled the creation of highly realistic and diverse synthetic datasets. These advancements not only enhance the quality and utility of synthetic health data but also expand its applicability across a wide range of use cases, from medical imaging to genomics. The integration of synthetic data into clinical workflows and drug development pipelines is accelerating time-to-market for new therapies and improving the reliability of predictive analytics, thereby contributing to better patient outcomes and operational efficiencies.

Another critical factor supporting market expansion is the growing emphasis on interoperability and data sharing across the healthcare ecosystem. Synthetic health data enables seamless collaboration between diverse stakeholders, including healthcare providers, insurers, and technology vendors, by eliminating privacy barriers. This collaborative environment fosters innovation in areas such as population health management, personalized medicine, and remote patient monitoring. Additionally, the adoption of synthetic data is helping to address the challenges of data scarcity and bias, particularly in underrepresented populations, ensuring that AI models and healthcare solutions are more equitable and effective.

From a regional perspective, North America leads the synthetic health data market, accounting for the largest revenue share in 2024. This dominance is attributed to the region’s advanced healthcare infrastructure, high adoption of digital health technologies, and strong presence of key market players. Europe is following closely, driven by rigorous data protection regulations and a rapidly growing research ecosystem. The Asia Pacific region is emerging as a high-growth market, fueled by increasing investments in healthcare technology, expanding clinical research activities, and rising awareness about the benefits of synthetic health data. Latin America and the Middle East & Africa are also witnessing steady growth, supported by government initiatives to modernize healthcare systems and improve data-driven decision-making.

Component Analysis

The synthetic health data market is segmented by component into software and services, each playing a pivotal role in shaping the industry landscape. The software segment encompasses platforms and tools designed to generate, manage, and validate synthetic health datasets. These solutions leverage advanced machine learning algorithms and generative models to produce high-fidelity synthetic data that closely mirrors

To support respiratory research, a synthetic asthma dataset was generated based on a real-world data, originally documenting 381 patients with physician-confirmed asthma who were admitted to secondary care at a single centre in 2019. The dataset is highly detailed, covering demographics, structured physiological data, medication records, and clinical outcomes. The synthetic version extends to 561 patients admitted over a year, offering insights into patient patterns, risk factors, and treatment strategies.

The dataset was created using the Synthetic Data Vault package, specifically employing the GAN synthesizer. Real data was first read and pre-processed, ensuring datetime columns were correctly parsed and identifiers were handled as strings. Metadata was defined to capture the schema, specifying field types and primary keys. This metadata guided the synthesizer in understanding the structure of the data. The GAN synthesizer was then fitted to the real data, learning the distributions and dependencies within. After fitting, the synthesizer generated synthetic data that mirrors the statistical properties and relationships of the original dataset.

Geography: The West Midlands has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.

Data set availability: Data access is available via the PIONEER Hub for projects which will benefit the public or patients. This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes. Data access can be provided to NHS, academic, commercial, policy and third sector organisations. Applications from SMEs are welcome. There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee. Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.

Available supplementary data: Real world data. Matched controls; ambulance and community data. Unstructured data (images). We can provide the dataset in OMOP and other common data models and can provide real-world data upon request.

Available supplementary support: Analytics, model build, validation & refinement; A.I. support. Data partner support for ETL (extract, transform & load) processes. Bespoke and “off the shelf” Trusted Research Environment build and run. Consultancy with clinical, patient & end-user and purchaser access/ support. Support for regulatory requirements. Cohort discovery. Data-driven trials and “fast screen” services to assess population size.