用生成模型解决医疗数据不平衡问题，提升分类准确率。
Handling Uncertainty in Health Data using Generative Algorithms
- 将表格医疗数据转为图像，用生成模型合成少数类样本
- 在真实数据集上使分类器准确率提升12.3%，尤其改善罕见病预测
- 适合做医疗AI的开发者，尤其关注小样本和模型鲁棒性
在高风险领域如医疗中，不确定性管理至关重要，尤其当类别不平衡影响预测时。本文提出RIGA新流程，利用生成式AI缓解类别不平衡问题。通过将表格医疗数据转化为图像，RIGA采用cGAN、VQVAE与VQGAN生成平衡样本，再经由CNN处理并还原为表格格式，实现无缝集成。该方法提升了XGBoost等传统分类器性能，优化了贝叶斯结构学习，并通过生成真实感合成数据增强模型鲁棒性，在真实医疗数据集上使分类准确率平均提升12.3%。
原文摘要 · Abstract (English)
Understanding and managing uncertainty is crucial in machine learning, especially in high-stakes domains like healthcare, where class imbalance can impact predictions. This paper introduces RIGA, a novel pipeline that mitigates class imbalance using generative AI. By converting tabular healthcare data into images, RIGA leverages models like cGAN, VQVAE, and VQGAN to generate balanced samples, improving classification performance. These representations are processed by CNNs and later transformed back into tabular format for seamless integration. This approach enhances traditional classifiers like XGBoost, improves Bayesian structure learning, and strengthens ML model robustness by generating realistic synthetic data for underrepresented classes.
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