ArticleSensors (Basel, Switzerland)2023
Evaluating Synthetic Medical Images Using Artificial Intelligence with the GAN Algorithm.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Building a Clinically Relevant and Technically Robust Synthetic Histopathology Dataset for Breast and Gastric Cancer.Journal of medical systems · 2026Article
- Synthetic Melanoma Image Generation and Evaluation Using Generative Adversarial Networks.Bioengineering (Basel, Switzerland) · 2026Article
- A Deep Learning Approach for Classifying Benign, Malignant, and Borderline Ovarian Tumors Using Convolutional Neural Networks and Generative Adversarial Networks.Medical sciences (Basel, Switzerland) · 2026Article
- Synthetic data augmentation for improving performance in deep learning models for anatomical landmark localization on the distal upper limb.Frontiers in bioengineering and biotechnology · 2026Article
- Cross-modality synthesis of ultra-widefield fluorescein angiography from ultra-widefield color fundus photography for diabetic retinopathy via UWFDR-GAN.Journal of translational medicine · 2025Article
- Self-improving generative foundation model for synthetic medical image generation and clinical applications.Nature medicine · 2025Article
- GAN-Based Novel Approach for Generating Synthetic Medical Tabular Data.Bioengineering (Basel, Switzerland) · 2024Article
- CNN-Based Kidney Segmentation Using a Modified CLAHE Algorithm.Sensors (Basel, Switzerland) · 2024Article
- Lightweight Super-Resolution Techniques in Medical Imaging: Bridging Quality and Computational Efficiency.Bioengineering (Basel, Switzerland) · 2024Article
- Insights and Considerations in Development and Performance Evaluation of Generative Adversarial Networks (GANs): What Radiologists Need to Know.Diagnostics (Basel, Switzerland) · 2024Review
- Enhancing Automated Brain Tumor Detection Accuracy Using Artificial Intelligence Approaches for Healthcare Environments.Bioengineering (Basel, Switzerland) · 2024Article
- Enhancing Fetal Anomaly Detection in Ultrasonography Images: A Review of Machine Learning-Based Approaches.Biomimetics (Basel, Switzerland) · 2023Review
- Machine Learning Applications in Sarcopenia Detection and Management: A Comprehensive Survey.Healthcare (Basel, Switzerland) · 2023Article
- New Approach for Generating Synthetic Medical Data to Predict Type 2 Diabetes.Bioengineering (Basel, Switzerland) · 2023Article
- Fire Detection and Notification Method in Ship Areas Using Deep Learning and Computer Vision Approaches.Sensors (Basel, Switzerland) · 2023Article
- Real-Time Deep Learning-Based Drowsiness Detection: Leveraging Computer-Vision and Eye-Blink Analyses for Enhanced Road Safety.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, considerable work has been conducted on the development of synthetic medical images, but there are no satisfactory methods for evaluating their medical suitability. Existing methods mainly evaluate the quality of noise in the images, and the similarity of the images to the real images used to generate them. For this purpose, they use feature maps of images extracted in different ways or distribution of images set. Then, the proximity of synthetic images to the real set is evaluated using different distance metrics. However, it is not possible to determine whether only one synthetic image was generated repeatedly, or whether the synthetic set exactly repeats the training set. In addition, most evolution metrics take a lot of time to calculate. Taking these issues into account, we have proposed a method that can quantitatively and qualitatively evaluate synthetic images. This method is a combination of two methods, namely, FMD and CNN-based evaluation methods. The estimation methods were compared with the FID method, and it was found that the FMD method has a great advantage in terms of speed, while the CNN method has the ability to estimate more accurately. To evaluate the reliability of the methods, a dataset of different real images was checked.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.