What question did this study set out to answer?

To develop a deep learning framework that detects artifacts created by GANs in medical imaging.

February 21, 2026Open Access

Optimized Deep Learning Framework for Robust Detection of GAN-Induced Hallucinations in Medical Imaging

Key Points

To develop a deep learning framework that detects artifacts created by GANs in medical imaging.
Proposed a hybrid deep learning framework integrating statistical descriptors and semantic representations.
Employed a dual-stream approach for detecting pixel-level anomalies and structural inconsistencies.
Validated on a curated dataset of 10,000 medical images, including authentic and GAN-generated samples.
Incorporated domain adaptation strategies to improve generalizability in real-world settings.
Achieved 92.6% accuracy and an F1-score of 0.91 on synthetic test data.
Maintained 87.3% accuracy and an F1-score of 0.85 on real-world GAN-modified images.
Attained an AUC of 0.96 and average precision of 0.92, outperforming traditional GAN detection methods.

Abstract

Generative Adversarial Networks (GANs) have become valuable tools in medical imaging, enabling realistic image synthesis for enhancement, augmentation, and restoration. However, their integration into clinical workflows raises concerns, particularly the risk of subtle distortions or hallucinations that may undermine diagnostic accuracy and weaken trust in AI-assisted decision-making. To address this challenge, we propose a hybrid deep learning framework designed to detect GAN-induced artifacts in medical images, thereby reinforcing the reliability of AI-driven diagnostics. The framework integrates low-level statistical descriptors, including high-frequency residuals and Gray-Level Co-occurrence Matrix (GLCM) texture features, with high-level semantic representations extracted from a pre-trained ResNet18. This dual-stream approach enables detection of both pixel-level anomalies and structural inconsistencies introduced by GAN-based manipulation. We validated the framework on a curated dataset of 10,000 medical images, evenly split between authentic and GAN-generated samples across four modalities: MRI, CT, X-ray, and fundus photography. To improve generalizability to real-world clinical settings, we incorporated domain adaptation strategies such as adversarial training and style transfer, reducing domain shift by 15%. Experimental results demonstrate robust performance, achieving 92.6% accuracy and an F1-score of 0.91 on synthetic test data, and maintaining strong performance on real-world GAN-modified images with 87.3% accuracy and an F1-score of 0.85. Additionally, the model attained an AUC of 0.96 and an average precision of 0.92, outperforming conventional GAN detection pipelines and baseline Convolutional Neural Network (CNN) architectures. These findings establish the proposed framework as an effective and reliable solution for detecting GAN-induced hallucinations in medical imaging, representing an important step toward building trustworthy and clinically deployable AI systems.

Perguntar à IA

Bookmark

View Full Paper

Cite This Study

Amjad et al. (Thu,) studied this question.

synapsesocial.com/papers/69994b41873532290d01f76f https://doi.org/https://doi.org/10.32604/cmes.2026.073473

Perguntar à IA

Bookmark

View Full Paper