D-Cube：利用扩散模型的超特征进行鲁棒的医学分类
D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification
AI总结：
本文提出D-Cube方法，结合扩散模型的超特征与对比学习进行特征选择，有效解决了医学影像中数据不平衡和样本有限的问题，在CT、MRI和X射线等多种模态下实现了优于基线模型的癌症诊断准确率。
摘要：
深度学习技术在医学影像中的整合旨在提高癌症诊断的效率和准确性，特别是对于胰腺癌和乳腺癌，由于其高死亡率和复杂的影像特征，这些癌症带来了重大的诊断挑战。本文提出了Diffusion-Driven Diagnosis (D-Cube)（扩散驱动诊断），这是一种利用扩散模型的超特征并结合对比学习来改善癌症诊断的新方法。D-Cube采用先进的特征选择技术，利用扩散模型强大的表示能力，在数据不平衡和样本可用性有限等挑战性条件下，增强医学数据集上的分类性能。特征选择过程优化了临床相关特征的提取，显著提高了分类准确性，并证明了在数据不平衡和有限数据场景下的鲁棒性。实验结果验证了D-Cube在多种医学成像模态（包括CT、MRI和X射线）中的有效性，与现有的基线模型相比表现出卓越的性能。D-Cube代表了癌症检测中的一种新策略，采用先进的深度学习技术来实现最先进的诊断准确性和效率。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
