用扩散模型的高阶特征提升癌症诊断准确率，尤其适合数据少或不均衡的情况。
D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification
- 从扩散模型提取高阶特征，结合对比学习优化医学图像分类。
- 在CT、MRI、X-ray上均优于现有模型，数据少时仍保持高准确率。
- 特别适合胰腺癌和乳腺癌这类难诊断疾病，对小样本和不平衡数据鲁棒。
深度学习在医学影像中的应用旨在提升胰腺癌和乳腺癌等高致死率疾病的诊断效率与准确性。本文提出基于扩散模型的D-Cube方法，利用扩散模型生成的超特征结合对比学习，增强癌症诊断能力。该方法通过先进特征选择技术，挖掘扩散模型强大的表征能力，在数据不平衡和样本有限条件下显著提升分类性能。特征选择过程聚焦临床相关特征，有效改善分类准确率，且在多种医学影像模态（包括CT、MRI、X-ray）中表现出色。实验结果表明，D-Cube在多个基准测试中优于现有基线模型，实现了最先进的诊断准确率与效率，为癌症检测提供新策略。
原文摘要 · Abstract (English)
The integration of deep learning technologies in medical imaging aims to enhance the efficiency and accuracy of cancer diagnosis, particularly for pancreatic and breast cancers, which present significant diagnostic challenges due to their high mortality rates and complex imaging characteristics. This paper introduces Diffusion-Driven Diagnosis (D-Cube), a novel approach that leverages hyper-features from a diffusion model combined with contrastive learning to improve cancer diagnosis. D-Cube employs advanced feature selection techniques that utilize the robust representational capabilities of diffusion models, enhancing classification performance on medical datasets under challenging conditions such as data imbalance and limited sample availability. The feature selection process optimizes the extraction of clinically relevant features, significantly improving classification accuracy and demonstrating resilience in imbalanced and limited data scenarios. Experimental results validate the effectiveness of D-Cube across multiple medical imaging modalities, including CT, MRI, and X-ray, showing superior performance compared to existing baseline models. D-Cube represents a new strategy in cancer detection, employing advanced deep learning techniques to achieve state-of-the-art diagnostic accuracy and efficiency.
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