Clip-Medfake: Synthetic Data Augmentation With AI-Generated Content for Improved Medical Image Classification
Honghui Chen, Baoquan Zhao, Guanghui Yue, Weide Liu, Chenlei Lv, Ruomei Wang, Fan Zhou · 2024
Data augmentation is serving as a critical and fundamental technology to improve model generalization and performance in a wide spectrum of machine learning tasks. Despite the increasing interest in developing various pathways to artificially generate new data to reduce the overfitting issue during model training, enriching the diversity of training data in the field of medicine remains facing enormous challenges. By virtue of recent advancements in generative artificial intelligence, we present a novel data augmentation framework, CLIP-MedFake, to address the shortage of training data used in medical image classification. The proposed method first employs the Stable Diffusion model to generate new fake data based on a small amount of training data, and then adopts the paradigm of few-shot learning and uses the CLIP architecture as the backbone to pre-train the model with synthetic data and then fine-tune it with real medical images. Extensive experiment results on two publicly available datasets demonstrate the effectiveness of the proposed method in promoting medical image classification.