Automated data augmentation methods have significantly improved the performance and generalization capability of deep learning models in image classification. Yet, most state-of-the-art methods are optimized on common benchmark datasets, limiting their applicability to more diverse or domain-specific data, such as medical datasets. In this paper, we propose a strategy that uses large language models to automatically generate efficient augmentation policies, customized to fit the specific characteristics of any dataset and model architecture. The proposed method iteratively interacts with an LLM to obtain and refine the augmentation policies on model performance feedback, creating a dataset-agnostic data augmentation pipeline. The proposed method was evaluated on medical imaging datasets, showing a clear improvement over state-of-the-art methods. The proposed approach offers an adaptive and scalable solution. Although it increases computational cost, it significantly boosts model robustness, automates the process, and minimizes the need for human involvement during model development.

本研究解决了当前数据增强方法在应用于医疗数据集等多样化领域时的局限性。通过利用大型语言模型自动生成适应特定数据集和模型架构的增强策略，研究展现了一种自适应且可扩展的数据增强方案，显著提高了模型的鲁棒性和自动化水平。该方法在医学影像数据集上的评估结果表明，其性能明显优于现有的最先进方法。

基于大型语言模型的图像分类增强策略生成