扩散模型在皮肤与口腔癌图像分类中表现优异，媲美顶尖深度学习模型。
Diffusion models applied to skin and oral cancer classification
- 用扩散模型处理皮肤与口腔病变图像分类，无需复杂架构。
- 皮肤癌六分类平衡准确率64.57%，二分类达83.57%；口腔癌平衡准确率90.50%。
- 模型在真实临床数据上具鲁棒性，适合医学影像诊断应用。
本研究探讨了扩散模型在医学图像分类（DiffMIC）中的应用，聚焦于皮肤与口腔病变。基于PAD-UFES-20皮肤癌数据集和P-NDB-UFES口腔癌数据集，扩散模型在性能上与当前最先进的卷积神经网络（CNN）和Transformer相媲美。在PAD-UFES-20数据集上，六分类任务的平衡准确率为0.6457，二分类（癌症与非癌症）为0.8357；在P-NDB-UFES数据集上，平衡准确率达0.9050。结果表明，扩散模型是皮肤与口腔病变医学图像分类的有效工具。此外，还评估了在PAD-UFES-20上训练的模型在临床图像集HIBA上的鲁棒性。
原文摘要 · Abstract (English)
This study investigates the application of diffusion models in medical image classification (DiffMIC), focusing on skin and oral lesions. Utilizing the datasets PAD-UFES-20 for skin cancer and P-NDB-UFES for oral cancer, the diffusion model demonstrated competitive performance compared to state-of-the-art deep learning models like Convolutional Neural Networks (CNNs) and Transformers. Specifically, for the PAD-UFES-20 dataset, the model achieved a balanced accuracy of 0.6457 for six-class classification and 0.8357 for binary classification (cancer vs. non-cancer). For the P-NDB-UFES dataset, it attained a balanced accuracy of 0.9050. These results suggest that diffusion models are viable models for classifying medical images of skin and oral lesions. In addition, we investigate the robustness of the model trained on PAD-UFES-20 for skin cancer but tested on the clinical images of the HIBA dataset.
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