基于生成式AI的数据增强用于口腔病变分类：PhotoMOCI数据集与基准

中文摘要 AI 辅助

通过摄影成像进行口腔癌的早期检测为大规模口腔筛查提供了一条有前景的途径。然而，鲁棒深度学习模型的开发常常受到高质量、带标注数据集稀缺的阻碍。为解决这一局限，我们引入了一个新颖且精心整理的数据资源——多用途口腔癌摄影成像（PhotoMOCI）数据集，用于开发口腔肿瘤学中多个诊断任务的模型。随后，我们开展了一项全面的基准研究，以探究各种数据增强策略如何影响图像分类器的性能。我们的分析涵盖了不同的生成式AI框架，评估了传统方法与先进生成方法（包括生成对抗网络（GANs）和扩散模型（DMs））的有效性。此外，我们提出了合成图像过滤器（SIF），这是一种基于两个辅助模型来选择特定样本的机制：合成代理分类器用于确保样本代表目标类别，合成图像检测器用于验证样本看起来真实，从而仅选择有助于提升下游性能的高效用图像。在所评估的数据集和分类器中，最佳SIF过滤设置在所有情况下均优于传统增强，在PhotoMOCI上分别提升了+1.73%和+2.35%，在KOCD上对于ResNet50和ViT分别提升了+2.38%和+2.08%。我们的研究结果表明，虽然直接应用生成式数据增强可能导致性能下降，但结合SIF——同时考虑（i）合成数据看起来有多真实以及（ii）它如何反映所属类别的判别性特征——提供了一种简单而有效的机制，用于过滤掉在训练过程中混淆分类器的合成样本。

英文摘要

Early detection of oral cancer via photographic imaging presents a promising avenue for large-scale oral cavity screening. However, the development of robust deep learning models is frequently hampered by the scarcity of high-quality, annotated datasets. To address this limitation, a novel and well-curated resource, the Photographic Multi-purpose Oral Cancer Imaging (PhotoMOCI) dataset, is introduced for developing models across multiple diagnostic tasks in oral oncology. Then, a comprehensive benchmark study was conducted to investigate how various data augmentation strategies influence the performance of image classifiers. Our analysis spans different generative AI frameworks, evaluating the efficacy of traditional methods against advanced generative approaches, including Generative Adversarial Networks (GANs) and Diffusion Models (DMs). Additionally, we propose the Synthetic Image Filter (SIF), a mechanism to select specific samples based on two auxiliary models: Synthetic Proxy Classifier to ensure samples are representative of the target class and Synthetic Image Detector to verify they appear realistic, thereby selecting only the high-utility images that contribute to improving downstream performance. Across the evaluated datasets and classifiers, the best SIF-filtered setup improves accuracy over traditional augmentation in all cases, with gains of +1.73% and +2.35% on PhotoMOCI and +2.38% and +2.08% on KOCD for ResNet50 and ViT, respectively. Our findings reveal that while the direct application of generative data augmentation may yield performance drops, the integration of SIF, considering (i) how synthetic data looks real and (ii) how it reflects the discriminative features of the belonging class, provides a simple yet effective mechanism to filter out synthetic samples that confuse the classifier during training.

发表机构

• Aalborg University（奥尔堡大学）
• University of Pisa（比萨大学）
• University of Salerno（萨莱诺大学）

机构由 AI 辅助整理，请以论文原文为准。