AIMC Topic: Generative Adversarial Networks

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ECG synthesis for cardiac arrhythmias: Integrating self-supervised learning and generative adversarial networks.

Artificial intelligence in medicine
Arrhythmia classifiers relying on supervised deep learning models usually require a substantial amount of labeled clinical data. The distribution of these labels is strictly related to the statistics of cardiovascular diseases among the population, w...

Reconstructing Super-Resolution Raman Spectral Image Using a Generative Adversarial Network-Based Algorithm.

Analytical chemistry
Raman imaging utilizes molecular fingerprint information to visualize the spatial distribution of a substance within the scanned area. Subject to its scanning mechanism, it usually costs a prolonged data acquisition duration for achieving high-resolu...

Synthetic neurosurgical data generation with generative adversarial networks and large language models:an investigation on fidelity, utility, and privacy.

Neurosurgical focus
OBJECTIVE: Use of neurosurgical data for clinical research and machine learning (ML) model development is often limited by data availability, sample sizes, and regulatory constraints. Synthetic data offer a potential solution to challenges associated...

[Cross modal translation of magnetic resonance imaging and computed tomography images based on diffusion generative adversarial networks].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
To address the issues of difficulty in preserving anatomical structures, low realism of generated images, and loss of high-frequency image information in medical image cross-modal translation, this paper proposes a medical image cross-modal translati...

Generative adversarial networks in medical image reconstruction: A systematic literature review.

Computers in biology and medicine
PURPOSE: Recent advancements in generative adversarial networks (GANs) have demonstrated substantial potential in medical image processing. Despite this progress, reconstructing images from incomplete data remains a challenge, impacting image quality...

Energy and time-aware scheduling in diverse virtualized cloud computing environments using optimized self-attention progressive generative adversarial network.

Network (Bristol, England)
The rapid growth of cloud computing has led to the widespread adoption of heterogeneous virtualized environments, offering scalable and flexible resources to meet diverse user demands. However, the increasing complexity and variability in workload ch...

Multisequence 3-T Image Synthesis from 64-mT Low-Field-Strength MRI Using Generative Adversarial Networks in Multiple Sclerosis.

Radiology
Background Portable low-field-strength (64-mT) MRI scanners show promise for increasing access to neuroimaging for clinical and research purposes; however, these devices produce lower-quality images than conventional high-field-strength scanners. Pur...

Improved generative adversarial networks model for movie dance generation.

PloS one
To address the challenges of innovation and efficiency in film choreography, this study proposes a dance generation model based on the generative adversarial networks. The model is trained using the AIST++ dance motion dataset, incorporating data fro...

A super resolution generative adversarial networks and partition-based adaptive filtering technique for detect and remove flickers in digital color images.

PloS one
Eliminating flickering from digital images captured by cameras equipped with a rolling shutter is of paramount importance in computer vision applications. The ripple effect observed in an individual image is a consequence of the non-synchronized expo...