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Statistical Validation of Synthetic Data for Lung Cancer Patients Generated by Using Generative Adversarial Networks

https://doi.org/10.3390/electronics11203277

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-030-00536-8openalex-snapshot

Simulation and Synthesis in Medical Imaging

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Bioinformatics and Biomedical Engineering

inferredcites →https://doi.org/10.1016/j.ins.2022.07.066openalex-snapshot

Improving the quality of generative models through Smirnov transformation

inferredcites →https://doi.org/10.1016/j.patter.2020.100089openalex-snapshot

Generative Adversarial Networks in Digital Pathology: A Survey on Trends and Future Potential

inferredcites →https://doi.org/10.1109/access.2021.3049781openalex-snapshot

Synthetic CT Generation of the Pelvis in Patients With Cervical Cancer: A Single Input Approach Using Generative Adversarial Network

inferredcites →https://doi.org/10.1109/jbhi.2020.2980262openalex-snapshot

Anonymization Through Data Synthesis Using Generative Adversarial Networks (ADS-GAN)

inferredcites →https://doi.org/10.1109/jiot.2021.3051158openalex-snapshot

Digital Twin for Intelligent Context-Aware IoT Healthcare Systems

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Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review

inferredcites →https://doi.org/10.1145/3164541.3164583openalex-snapshot

Set-valued Data Anonymization Maintaining Data Utility and Data Property

inferredcites →https://doi.org/10.1186/2043-9113-5-s1-s2openalex-snapshot

OpenClinica

inferredcites →https://doi.org/10.1186/s12911-017-0499-0openalex-snapshot

Utility-preserving anonymization for health data publishing

inferredcites →https://doi.org/10.1186/s12911-019-0793-0openalex-snapshot

The validity of synthetic clinical data: a validation study of a leading synthetic data generator (Synthea) using clinical quality measures

inferredcites →https://doi.org/10.1200/cci.20.00072openalex-snapshot

Machine Learning in Oncology: Methods, Applications, and Challenges

inferredcites →https://doi.org/10.2196/35734openalex-snapshot

Utility Metrics for Evaluating Synthetic Health Data Generation Methods: Validation Study

inferredcites →https://doi.org/10.3233/faia190139openalex-snapshot

Towards a Healthcare Digital Twin1

inferredcites →https://doi.org/10.3389/fgene.2018.00031openalex-snapshot

Digital Twins in Health Care: Ethical Implications of an Emerging Engineering Paradigm

inferredcites →https://doi.org/10.3390/electronics10040389openalex-snapshot

Generating Synthetic ECGs Using GANs for Anonymizing Healthcare Data

inferredcites →https://doi.org/10.3390/electronics10182220openalex-snapshot

Generative Adversarial Networks for Anonymized Healthcare of Lung Cancer Patients

inferredcites →https://doi.org/10.36227/techrxiv.16610896.v1openalex-snapshot

Standardised Metrics and Methods for Synthetic Tabular Data Evaluation

inferredcites →https://doi.org/10.4103/jpi.jpi_103_20openalex-snapshot

Generative Adversarial Networks in Digital Pathology and Histopathological Image Processing: A Review

inferredcites →https://doi.org/10.48550/arxiv.1706.08500openalex-snapshot

GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Ethics and Regulations in Generative AI

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Optimizing Classifier-Based Synthetic Data Generation for Lung Cancer Prediction

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Enhancing date fruit classification using machine learning, CTGAN, and SHAP-based explainability

inferred← citeshttps://doi.org/10.1016/j.ejmp.2025.104914openalex-snapshot

Deep learning paradigms in lung cancer diagnosis: A methodological review, open challenges, and future directions

inferred← citeshttps://doi.org/10.1016/j.ijmedinf.2024.105413openalex-snapshot

Can I trust my fake data – A comprehensive quality assessment framework for synthetic tabular data in healthcare

inferred← citeshttps://doi.org/10.1109/icaiss61471.2025.11042018openalex-snapshot

Integrating Generative Adversarial Networks (GANs) with Deep Learning for Improved Lung Cancer Detection from CT Scans

inferred← citeshttps://doi.org/10.1109/ictcs65341.2025.10989480openalex-snapshot

Applications of Digital Twin in Precision Medicine: A Systematic Review

inferred← citeshttps://doi.org/10.1186/s12967-025-06371-zopenalex-snapshot

From virtual to reality: innovative practices of digital twins in tumor therapy

inferred← citeshttps://doi.org/10.1200/cci.23.00045openalex-snapshot

Opportunities and Challenges of Synthetic Data Generation in Oncology

inferred← citeshttps://doi.org/10.2139/ssrn.6283638openalex-snapshot

Synthetic data improves survival model evaluation under distribution shifts

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Generative Adversarial Networks in Business and Social Science

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From Manipulation to Antidote: Mapping the Computational Capabilities of AI-Generated Synthetic Media to Health-Related Applications and Downstream Benefits

inferred← citeshttps://doi.org/10.3390/electronics12071601openalex-snapshot

Nonparametric Generation of Synthetic Data Using Copulas

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Applying a Digital Twin Approach for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome

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