用扩散模型生成超声图像，提升乳腺癌原位癌升级预测准确率
DiffKD-DCIS: Predicting Upgrade of Ductal Carcinoma In Situ with Diffusion Augmentation and Knowledge Distillation
- 通过条件扩散模型生成高质量超声图像扩充数据
- 学生网络参数少、推理快，在外部测试集上超越多数方法
- 临床效果接近资深放射科医生，适合辅助基层医疗
准确预测导管原位癌（DCIS）向浸润性导管癌（IDC）的升级对术前规划至关重要。然而，传统深度学习方法受限于超声数据量少和泛化能力差。本研究提出DiffKD-DCIS框架，融合条件扩散建模与教师-学生知识蒸馏。该框架分三阶段运行：首先，利用多模态条件生成高保真超声图像进行数据增强；其次，深度教师网络从原始与合成数据中提取鲁棒特征；最后，紧凑的学生网络通过知识蒸馏学习教师模型，兼顾泛化性与计算效率。在包含1,435例病例的多中心数据集上评估，合成图像质量良好。学生网络参数更少、推理更快。在外部测试集上，其性能优于部分组合方法，准确率接近资深放射科医生，高于初级医生，展现出显著临床潜力。
原文摘要 · Abstract (English)
Accurately predicting the upgrade of ductal carcinoma in situ (DCIS) to invasive ductal carcinoma (IDC) is crucial for surgical planning. However, traditional deep learning methods face challenges due to limited ultrasound data and poor generalization ability. This study proposes the DiffKD-DCIS framework, integrating conditional diffusion modeling with teacher-student knowledge distillation. The framework operates in three stages: First, a conditional diffusion model generates high-fidelity ultrasound images using multimodal conditions for data augmentation. Then, a deep teacher network extracts robust features from both original and synthetic data. Finally, a compact student network learns from the teacher via knowledge distillation, balancing generalization and computational efficiency. Evaluated on a multi-center dataset of 1,435 cases, the synthetic images were of good quality. The student network had fewer parameters and faster inference. On external test sets, it outperformed partial combinations, and its accuracy was comparable to senior radiologists and superior to junior ones, showing significant clinical potential.
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