Abstract
In oral cancer diagnostics, the limited availability of annotated datasets frequently constrains the performance of diagnostic models, particularly due to the variability and insufficiency of training data. To address these challenges, this study proposed a novel approach to enhance diagnostic accuracy by synthesizing realistic oral cancer lesions using an inpainting technique with a fine-tuned diffusion model. We compiled a comprehensive dataset from multiple sources, featuring a variety of oral cancer images. Our method generated synthetic lesions that exhibit a high degree of visual fidelity to actual lesions, thereby significantly enhancing the performance of diagnostic algorithms. The results show that our classification model achieved a diagnostic accuracy of 0.97 in differentiating between cancerous and non-cancerous tissues, while our detection model accurately identified lesion locations with 0.85 accuracy. This method validates the potential for synthetic image generation in medical diagnostics and paves the way for further research into extending these methods to other types of cancer diagnostics.
Abstract (translated)
在口腔癌诊断中，由于注释数据集的有限可用性和训练数据的变化性及不足，限制了诊断模型的表现。为解决这些问题，本研究提出了一种新方法，利用经过微调的扩散模型和图像修复技术生成逼真的口腔癌症病变图像，以提高诊断准确性。我们从多个来源编译了一个包含多种类型口腔癌图像的综合数据集。我们的方法生成的合成病变与实际病变具有高度视觉相似性，从而显著提高了诊断算法的表现。 实验结果显示，我们的分类模型在区分癌症和非癌症组织方面的准确率为0.97，而检测模型在识别病灶位置上的准确率达到了0.85。这一方法验证了使用合成图像生成技术在医学诊断中的潜力，并为将这些方法扩展应用于其他类型癌症的诊断研究铺平道路。
URL
https://arxiv.org/abs/2508.06151
