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 rais...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.展开更多
Single-crystal GaN epilayers were irradiated with heavy inert gas ions(2.3-MeV Ne^(8+),5.3-MeV Kr^(19+))to fluences ranging from 1.0×1.0^(11) to 1.0×1.0^(15)ions∕cm^(2).The strain-related damage accumulatio...Single-crystal GaN epilayers were irradiated with heavy inert gas ions(2.3-MeV Ne^(8+),5.3-MeV Kr^(19+))to fluences ranging from 1.0×1.0^(11) to 1.0×1.0^(15)ions∕cm^(2).The strain-related damage accumulation versus ion fluences was studied using highresolution X-ray diffraction(HRXRD)and ultraviolet–visible(UV–Vis)spectroscopy.The results showed that the damage accumulation was mainly dominated by nuclear energy loss.When the ion fluence was less than∼0.055 displacement per atom(dpa),the lattice expansions and lattice strains markedly increased linearly with increasing ion fluences,accompanied by a slow enhancement in the dislocation densities,distortion parameters,and Urbach energy for both ion irradiations.Above this fluence(∼0.055 dpa),the lattice strains presented a slight increase,whereas a remarkable increase was observed in the dislocation densities,distortion parameters,and Urbach energy with the ion fluences after both ion irradiations.∼0.055 dpa is the threshold ion fluence for defect evolution and lattice damage related to strain.The mechanisms underlying the damage accumulation are discussed in detail.展开更多
With the rapid advancement of optoelectronic technology,high-performance photodetectors are increasingly in demand in fields such as environmental monitoring,optical communication,and defense systems,where ultraviolet...With the rapid advancement of optoelectronic technology,high-performance photodetectors are increasingly in demand in fields such as environmental monitoring,optical communication,and defense systems,where ultraviolet detection is critical.However,conventional semiconductor materials suffer from limited UV-visible detection capabilities owing to their narrow bandgaps and high dark currents.To address these challenges,wide-bandgap semiconductors have emerged as promising alternatives.Here,we fabricated a horizontally structured n–n heterojunction photodetector by growingβ-Ga_(2)O_(3) on Si–GaN via plasma-enhanced chemical vapor deposition.The device exhibits a self-powered photocurrent of 3.5 nA at zero bias,enabled by the photovoltaic effect of the space charge region.Under 254-nm and 365-nm illumination,it exhibits rectification behavior,achieving a responsivity of 0.475 m A/W(0 V,220??W/cm~2 at 254 nm)and 257.6 mA/W(-5 V),respectively.Notably,the photodetector demonstrates a high photocurrent-to-dark current ratio of 10~5 under-5-V bias,highlighting its potential for self-powered and high-performance UV detection applications.展开更多
The growing spectrum of Generative Adversarial Network (GAN) applications in medical imaging, cyber security, data augmentation, and the field of remote sensing tasks necessitate a sharp spike in the criticality of re...The growing spectrum of Generative Adversarial Network (GAN) applications in medical imaging, cyber security, data augmentation, and the field of remote sensing tasks necessitate a sharp spike in the criticality of review of Generative Adversarial Networks. Earlier reviews that targeted reviewing certain architecture of the GAN or emphasizing a specific application-oriented area have done so in a narrow spirit and lacked the systematic comparative analysis of the models’ performance metrics. Numerous reviews do not apply standardized frameworks, showing gaps in the efficiency evaluation of GANs, training stability, and suitability for specific tasks. In this work, a systemic review of GAN models using the PRISMA framework is developed in detail to fill the gap by structurally evaluating GAN architectures. A wide variety of GAN models have been discussed in this review, starting from the basic Conditional GAN, Wasserstein GAN, and Deep Convolutional GAN, and have gone down to many specialized models, such as EVAGAN, FCGAN, and SIF-GAN, for different applications across various domains like fault diagnosis, network security, medical imaging, and image segmentation. The PRISMA methodology systematically filters relevant studies by inclusion and exclusion criteria to ensure transparency and replicability in the review process. Hence, all models are assessed relative to specific performance metrics such as accuracy, stability, and computational efficiency. There are multiple benefits to using the PRISMA approach in this setup. Not only does this help in finding optimal models suitable for various applications, but it also provides an explicit framework for comparing GAN performance. In addition to this, diverse types of GAN are included to ensure a comprehensive view of the state-of-the-art techniques. This work is essential not only in terms of its result but also because it guides the direction of future research by pinpointing which types of applications require some GAN architectures, works to improve specific task model selection, and points out areas for further research on the development and application of GANs.展开更多
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2601).
文摘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.
基金supported by the Program for National Natural Science Foundation of China(No.11675231)the Sichuan Science and Technology Program(Nos.2022YFG0263 and 2024NSFSC1097)the Scientific Research Starting Foundation for talents(Nos.21zx7109 and 22zx7175,24ycx1005).
文摘Single-crystal GaN epilayers were irradiated with heavy inert gas ions(2.3-MeV Ne^(8+),5.3-MeV Kr^(19+))to fluences ranging from 1.0×1.0^(11) to 1.0×1.0^(15)ions∕cm^(2).The strain-related damage accumulation versus ion fluences was studied using highresolution X-ray diffraction(HRXRD)and ultraviolet–visible(UV–Vis)spectroscopy.The results showed that the damage accumulation was mainly dominated by nuclear energy loss.When the ion fluence was less than∼0.055 displacement per atom(dpa),the lattice expansions and lattice strains markedly increased linearly with increasing ion fluences,accompanied by a slow enhancement in the dislocation densities,distortion parameters,and Urbach energy for both ion irradiations.Above this fluence(∼0.055 dpa),the lattice strains presented a slight increase,whereas a remarkable increase was observed in the dislocation densities,distortion parameters,and Urbach energy with the ion fluences after both ion irradiations.∼0.055 dpa is the threshold ion fluence for defect evolution and lattice damage related to strain.The mechanisms underlying the damage accumulation are discussed in detail.
基金Project supported by the Joints Fund of the National Natural Science Foundation of China(Grant No.U23A20349)the Young Scientists Fund of the National Natural Science Foundation of China(Grant Nos.62204126,62305171,62304113)。
文摘With the rapid advancement of optoelectronic technology,high-performance photodetectors are increasingly in demand in fields such as environmental monitoring,optical communication,and defense systems,where ultraviolet detection is critical.However,conventional semiconductor materials suffer from limited UV-visible detection capabilities owing to their narrow bandgaps and high dark currents.To address these challenges,wide-bandgap semiconductors have emerged as promising alternatives.Here,we fabricated a horizontally structured n–n heterojunction photodetector by growingβ-Ga_(2)O_(3) on Si–GaN via plasma-enhanced chemical vapor deposition.The device exhibits a self-powered photocurrent of 3.5 nA at zero bias,enabled by the photovoltaic effect of the space charge region.Under 254-nm and 365-nm illumination,it exhibits rectification behavior,achieving a responsivity of 0.475 m A/W(0 V,220??W/cm~2 at 254 nm)and 257.6 mA/W(-5 V),respectively.Notably,the photodetector demonstrates a high photocurrent-to-dark current ratio of 10~5 under-5-V bias,highlighting its potential for self-powered and high-performance UV detection applications.
文摘The growing spectrum of Generative Adversarial Network (GAN) applications in medical imaging, cyber security, data augmentation, and the field of remote sensing tasks necessitate a sharp spike in the criticality of review of Generative Adversarial Networks. Earlier reviews that targeted reviewing certain architecture of the GAN or emphasizing a specific application-oriented area have done so in a narrow spirit and lacked the systematic comparative analysis of the models’ performance metrics. Numerous reviews do not apply standardized frameworks, showing gaps in the efficiency evaluation of GANs, training stability, and suitability for specific tasks. In this work, a systemic review of GAN models using the PRISMA framework is developed in detail to fill the gap by structurally evaluating GAN architectures. A wide variety of GAN models have been discussed in this review, starting from the basic Conditional GAN, Wasserstein GAN, and Deep Convolutional GAN, and have gone down to many specialized models, such as EVAGAN, FCGAN, and SIF-GAN, for different applications across various domains like fault diagnosis, network security, medical imaging, and image segmentation. The PRISMA methodology systematically filters relevant studies by inclusion and exclusion criteria to ensure transparency and replicability in the review process. Hence, all models are assessed relative to specific performance metrics such as accuracy, stability, and computational efficiency. There are multiple benefits to using the PRISMA approach in this setup. Not only does this help in finding optimal models suitable for various applications, but it also provides an explicit framework for comparing GAN performance. In addition to this, diverse types of GAN are included to ensure a comprehensive view of the state-of-the-art techniques. This work is essential not only in terms of its result but also because it guides the direction of future research by pinpointing which types of applications require some GAN architectures, works to improve specific task model selection, and points out areas for further research on the development and application of GANs.
