FakeNews: GAN-based generation of realistic 3D volumetric data -- A systematic review and taxonomy

• URL: http://arxiv.org/abs/2207.01390v2
• Date: Wed, 14 Feb 2024 11:03:38 GMT
• Title: FakeNews: GAN-based generation of realistic 3D volumetric data -- A systematic review and taxonomy
• Authors: Andr\'e Ferreira, Jianning Li, Kelsey L. Pomykala, Jens Kleesiek, Victor Alves, Jan Egger
• Abstract summary: Generative Adversarial Networks (GANs) are used to generate realistic synthetic data. In this review, we provide a summary of works that generate realistic volumetric synthetic data using GANs.
• Score: 2.801317303396674
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the massive proliferation of data-driven algorithms, such as deep learning-based approaches, the availability of high-quality data is of great interest. Volumetric data is very important in medicine, as it ranges from disease diagnoses to therapy monitoring. When the dataset is sufficient, models can be trained to help doctors with these tasks. Unfortunately, there are scenarios where large amounts of data is unavailable. For example, rare diseases and privacy issues can lead to restricted data availability. In non-medical fields, the high cost of obtaining enough high-quality data can also be a concern. A solution to these problems can be the generation of realistic synthetic data using Generative Adversarial Networks (GANs). The existence of these mechanisms is a good asset, especially in healthcare, as the data must be of good quality, realistic, and without privacy issues. Therefore, most of the publications on volumetric GANs are within the medical domain. In this review, we provide a summary of works that generate realistic volumetric synthetic data using GANs. We therefore outline GAN-based methods in these areas with common architectures, loss functions and evaluation metrics, including their advantages and disadvantages. We present a novel taxonomy, evaluations, challenges, and research opportunities to provide a holistic overview of the current state of volumetric GANs.

Related papers

• Synthetic Data in Radiological Imaging: Current State and Future Outlook [3.047958668050099]
  Key challenge for the development and deployment of artificial intelligence (AI) solutions in radiology is solving the associated data limitations. In silico data offers a number of potential advantages to patient data, such as diminished patient harm, reduced cost, simplified data acquisition, scalability, improved quality assurance testing, and a mitigation approach to data imbalances.
  arXiv  Detail & Related papers  (2024-05-08T18:35:47Z)
• How Good Are Synthetic Medical Images? An Empirical Study with Lung Ultrasound [0.3312417881789094]
  Adding synthetic training data using generative models offers a low-cost method to deal with the data scarcity challenge. We show that training with both synthetic and real data outperforms training with real data alone.
  arXiv  Detail & Related papers  (2023-10-05T15:42:53Z)
• MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data Augmentation [58.93221876843639]
  This paper introduces a novel, end-to-end diffusion-based risk prediction model, named MedDiffusion. It enhances risk prediction performance by creating synthetic patient data during training to enlarge sample space. It discerns hidden relationships between patient visits using a step-wise attention mechanism, enabling the model to automatically retain the most vital information for generating high-quality data.
  arXiv  Detail & Related papers  (2023-10-04T01:36:30Z)
• Generative models improve fairness of medical classifiers under distribution shifts [49.10233060774818]
  We show that learning realistic augmentations automatically from data is possible in a label-efficient manner using generative models. We demonstrate that these learned augmentations can surpass ones by making models more robust and statistically fair in- and out-of-distribution.
  arXiv  Detail & Related papers  (2023-04-18T18:15:38Z)
• Synthetic Data in Healthcare [10.555189948915492]
  We present the cases for physical and statistical simulations for creating data and the proposed applications in healthcare and medicine. We discuss that while synthetics can promote privacy, equity, safety and continual and causal learning, they also run the risk of introducing flaws, blind spots and propagating or exaggerating biases.
  arXiv  Detail & Related papers  (2023-04-06T17:23:39Z)
• Non-Imaging Medical Data Synthesis for Trustworthy AI: A Comprehensive Survey [6.277848092408045]
  Data quality is the key factor for the development of trustworthy AI in healthcare. Access to good quality datasets is limited by the technical difficulty of data acquisition. Large-scale sharing of healthcare data is hindered by strict ethical restrictions.
  arXiv  Detail & Related papers  (2022-09-17T13:34:17Z)
• Data-Efficient Learning via Minimizing Hyperspherical Energy [48.47217827782576]
  This paper considers the problem of data-efficient learning from scratch using a small amount of representative data. We propose a MHE-based active learning (MHEAL) algorithm, and provide comprehensive theoretical guarantees for MHEAL.
  arXiv  Detail & Related papers  (2022-06-30T11:39:12Z)
• Federated Learning Enables Big Data for Rare Cancer Boundary Detection [98.5549882883963]
  We present findings from the largest Federated ML study to-date, involving data from 71 healthcare institutions across 6 continents. We generate an automatic tumor boundary detector for the rare disease of glioblastoma. We demonstrate a 33% improvement over a publicly trained model to delineate the surgically targetable tumor, and 23% improvement over the tumor's entire extent.
  arXiv  Detail & Related papers  (2022-04-22T17:27:00Z)
• Synthetic Data: Opening the data floodgates to enable faster, more directed development of machine learning methods [96.92041573661407]
  Many ground-breaking advancements in machine learning can be attributed to the availability of a large volume of rich data. Many large-scale datasets are highly sensitive, such as healthcare data, and are not widely available to the machine learning community. Generating synthetic data with privacy guarantees provides one such solution.
  arXiv  Detail & Related papers  (2020-12-08T17:26:10Z)
• Fader Networks for domain adaptation on fMRI: ABIDE-II study [68.5481471934606]
  We use 3D convolutional autoencoders to build the domain irrelevant latent space image representation and demonstrate this method to outperform existing approaches on ABIDE data.
  arXiv  Detail & Related papers  (2020-10-14T16:50:50Z)
• Synthetic Observational Health Data with GANs: from slow adoption to a boom in medical research and ultimately digital twins? [0.16244541005112745]
  Vast potential is unexploited because of the fiercely private nature of patient-related data and regulations to protect it. Generative Adversarial Networks (GANs) have recently emerged as a groundbreaking way to learn generative models that produce realistic synthetic data. GANs posses capabilities relevant to common problems in healthcare: lack of data, class imbalance, rare diseases, and preserving privacy.
  arXiv  Detail & Related papers  (2020-05-27T17:40:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.