mlsanityData to 7 Oct 2026

Enhancing Medical Imaging with GANs Synthesizing Realistic Images from Limited Data

arXiv 2406.18547 ↗First posted 22 May 2024eess.IV cs.CVcited by 11

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Abstract (arXiv)

In this research, we introduce an innovative method for synthesizing medical images using generative adversarial networks (GANs). Our proposed GANs method demonstrates the capability to produce realistic synthetic images even when trained on a limited quantity of real medical image data, showcasing commendable generalization prowess. To achieve this, we devised a generator and discriminator network architecture founded on deep convolutional neural networks (CNNs), leveraging the adversarial training paradigm for model optimization. Through extensive experimentation across diverse medical image datasets, our method exhibits robust performance, consistently generating synthetic images that closely emulate the structural and textural attributes of authentic medical images.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 16 of its 28 references that are in the map. First posted on arXiv 22 May 2024.

Builds on

16 of 28 references.

References · 16

  1. 2014Image Super-Resolution Using Deep Convolutional Networkscited by 1,218
  2. 2017Deep MR to CT Synthesis using Unpaired Datacited by 86
  3. 2024Survival Prediction Across Diverse Cancer Types Using Neural Networkscited by 80
  4. 2023Deception Detection from Linguistic and Physiological Data Streams Using Bimodal Convolutional Neural Networkscited by 54
  5. 2024Convolutional neural network classification of cancer cytopathology images: taking breast cancer as an examplecited by 50
  6. 2023Generative Text-Guided 3D Vision-Language Pretraining for Unified Medical Image Segmentationcited by 47
  7. 2024Exploring Diverse Methods in Visual Question Answeringcited by 45
  8. 2023Improving Depth Gradient Continuity in Transformers: A Comparative Study on Monocular Depth Estimation with CNNcited by 35
  9. 2024Regional Style and Color Transfercited by 30
  10. 20233D-Aware Neural Body Fitting for Occlusion Robust 3D Human Pose Estimationcited by 28
  11. 2021An Optimization-Based Meta-Learning Model for MRI Reconstruction with Diverse Datasetcited by 18
  12. 2018Generative Adversarial Network for Medical Images (MI-GAN)cited by 17
4 more
  1. 2019Constrained Attractor Selection Using Deep Reinforcement Learningcited by 17
  2. 2020Attractor Selection in Nonlinear Energy Harvesting Using Deep Reinforcement Learningcited by 15
  3. 2024DF-DM: A foundational process model for multimodal data fusion in the artificial intelligence eracited by 9
  4. 2017CT-SRCNN: Cascade Trained and Trimmed Deep Convolutional Neural Networks for Image Super Resolutioncited by 3

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Cited by year

Citing papers per year: 2024: 9; 2025: 2.

11 papers.

  1. 2024Dynamic Hypergraph-Enhanced Prediction of Sequential Medical Visitscited by 12
  2. 2024A Lightweight GAN-Based Image Fusion Algorithm for Visible and Infrared Imagescited by 11
  3. 2024Wasserstein Distance-Weighted Adversarial Network for Cross-Domain Credit Risk Assessmentcited by 11
  4. 2024Text classification optimization algorithm based on graph neural networkcited by 8
  5. 2024Convolutional Neural Networks for Predictive Modeling of Lung Diseasecited by 7
  6. 2024Reducing Bias in Deep Learning Optimization: The RSGDM Approachcited by 7
  7. 2024Optimizing News Text Classification with Bi-LSTM and Attention Mechanism for Efficient Data Processingcited by 7
  8. 2024Deep Learning-Based Channel Squeeze U-Structure for Lung Nodule Detection and Segmentationcited by 6
  9. 2024Stock Type Prediction Model Based on Hierarchical Graph Neural Networkcited by 5
  10. 2025Patient-Zero: Scaling Synthetic Patient Agents to Real-World Distributions without Real Patient Datacited by 2
  11. 2025Leveraging Modified Ex Situ Tomography Data for Segmentation of In Situ Synchrotron X-Ray Computed Tomographycited by 1