Special Issue "Synthetic Medical Data for Machine Learning"

A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Biochemistry, Biophysics and Computational Biology".

Deadline for manuscript submissions: closed (25 August 2023) | Viewed by 721

Special Issue Editors

SimulaMet, 0167 Oslo, Norway
Interests: deep generative models; synthetic health data; general machine learning/ deep learning

Special Issue Information

Dear Colleagues,

Over the last decade, synthetic data generation has become increasingly popular after many successful deep generative models were developed to generate realistic artificial data. Synthetic text, images, and videos can be generated using deep generative adversarial networks and diffusion probabilistic models. Synthetic data plays a significant role in overcoming privacy concerns as well as the medical domain’s costly and time-consuming annotation process. In addition, synthetic data can overcome the lack of medical data with pathological findings to train machine learning models. Consequently, deficiency issues in the medical data can be addressed. Furthermore, generating synthetic data is essential for research on simulations and digital twins of the human body.  Therefore, synthetic medical data can help improve the generalizability of machine learning models, improving their clarity and causality while supporting the development of physiological simulations. In this Special Issue, we would like to see novel contributions to synthetic data generation (time series data, image, video, and select types of medical data such as fMRI), novel case studies in the medical domain, and improvements to machine learning algorithms in medicine. Some areas of interest for this Special Issue include:

  • Novel techniques for generating synthetic data, including advancements in deep generative models, diffusion probabilistic models, and other techniques.
  • Medical case studies using synthetic data: specifically studies that illustrate the use of synthetic medical data in various clinical settings.
  • Machine learning algorithm improvements that are specifically designed for medical applications.
  • Exploring issues relating to data privacy and security, including perspectives from ethics and law.
  • Physiological simulations and digital twins, and the use of synthetic data to develop physiological simulations and digital twins of the human body.
  • Novel datasets that either combine real and synthetic data or are completely synthetic.

Dr. Vajira Lasantha Thambawita
Prof. Dr. Michael Alexander Riegler
Guest Editors

Manuscript Submission Information

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  • synthetic medical data
  • deep generative models
  • synthesis of medical data

Published Papers (1 paper)

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Improving Structural MRI Preprocessing with Hybrid Transformer GANs
Life 2023, 13(9), 1893; https://doi.org/10.3390/life13091893 - 11 Sep 2023
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Magnetic resonance imaging (MRI) is a technique that is widely used in practice to evaluate any pathologies in the human body. One of the areas of interest is the human brain. Naturally, MR images are low-resolution and contain noise due to signal interference, [...] Read more.
Magnetic resonance imaging (MRI) is a technique that is widely used in practice to evaluate any pathologies in the human body. One of the areas of interest is the human brain. Naturally, MR images are low-resolution and contain noise due to signal interference, the patient’s body’s radio-frequency emissions and smaller Tesla coil counts in the machinery. There is a need to solve this problem, as MR tomographs that have the capability of capturing high-resolution images are extremely expensive and the length of the procedure to capture such images increases by the order of magnitude. Vision transformers have lately shown state-of-the-art results in super-resolution tasks; therefore, we decided to evaluate whether we can employ them for structural MRI super-resolution tasks. A literature review showed that similar methods do not focus on perceptual image quality because upscaled images are often blurry and are subjectively of poor quality. Knowing this, we propose a methodology called HR-MRI-GAN, which is a hybrid transformer generative adversarial network capable of increasing resolution and removing noise from 2D T1w MRI slice images. Experiments show that our method quantitatively outperforms other SOTA methods in terms of perceptual image quality and is capable of subjectively generalizing to unseen data. During the experiments, we additionally identified that the visual saliency-induced index metric is not applicable to MRI perceptual quality assessment and that general-purpose denoising networks are effective when removing noise from MR images. Full article
(This article belongs to the Special Issue Synthetic Medical Data for Machine Learning)
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