用扩散模型生成真实口腔癌病灶图像，提升诊断准确率。
Improving Diagnostic Accuracy for Oral Cancer with inpainting Synthesis Lesions Generated Using Diffusion Models
- 用微调扩散模型进行图像修复式病灶合成。
- 分类准确率达0.97，定位准确率达0.85。
- 适合医学影像数据不足场景下的诊断模型训练。
在口腔癌诊断中，标注数据集的稀缺性常制约诊断模型性能，尤其因训练数据的变异性与不足。为此，本研究提出一种新方法：利用微调后的扩散模型结合图像修复技术，合成逼真的口腔癌病灶图像，以提升诊断准确性。研究整合了多源数据，构建了涵盖多种口腔癌图像的综合性数据集。所生成的合成病灶在视觉上高度逼近真实病变，显著增强了诊断算法的表现。结果表明，分类模型在区分癌变与非癌变组织时达到0.97的准确率，检测模型在定位病灶方面实现0.85的准确率。该方法验证了合成图像生成在医学诊断中的潜力，为扩展至其他癌症诊断研究提供了可行路径。
原文摘要 · Abstract (English)
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.
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