用扩散模型与强化学习，一键定位3D超声切面并诊断子宫畸形。
Uncertainty-aware Diffusion and Reinforcement Learning for Joint Plane Localization and Anomaly Diagnosis in 3D Ultrasound
- 设计带局部全局引导的去噪扩散模型，自适应调整注意力分配。
- 通过无监督奖励机制提取关键切片，提升多平面信息融合效率。
- 引入文本驱动不确定性建模，动态优化分类结果，适合临床辅助诊断。
先天性子宫畸形（CUA）可导致不孕、流产、早产及妊娠并发症风险升高。相较于传统2D超声，3D超声可重建冠状面，清晰展现子宫形态，有助于准确评估CUA。本文提出一种智能系统，实现3D超声中子宫切面自动定位与畸形诊断的联合处理。主要贡献包括：1）构建具有局部（切面）与全局（体积/文本）引导的去噪扩散模型，采用自适应加权策略优化不同条件下的注意力分配；2）提出基于强化学习的框架，利用无监督奖励从冗余序列中提取关键切片，充分整合多平面信息以降低学习难度；3）引入文本驱动的不确定性建模，对粗略预测结果进行校正，进而提升整体分类性能。在大规模3D子宫超声数据集上的实验验证了方法在切面定位与畸形诊断方面的有效性。代码已开源。
原文摘要 · Abstract (English)
Congenital uterine anomalies (CUAs) can lead to infertility, miscarriage, preterm birth, and an increased risk of pregnancy complications. Compared to traditional 2D ultrasound (US), 3D US can reconstruct the coronal plane, providing a clear visualization of the uterine morphology for assessing CUAs accurately. In this paper, we propose an intelligent system for simultaneous automated plane localization and CUA diagnosis. Our highlights are: 1) we develop a denoising diffusion model with local (plane) and global (volume/text) guidance, using an adaptive weighting strategy to optimize attention allocation to different conditions; 2) we introduce a reinforcement learning-based framework with unsupervised rewards to extract the key slice summary from redundant sequences, fully integrating information across multiple planes to reduce learning difficulty; 3) we provide text-driven uncertainty modeling for coarse prediction, and leverage it to adjust the classification probability for overall performance improvement. Extensive experiments on a large 3D uterine US dataset show the efficacy of our method, in terms of plane localization and CUA diagnosis. Code is available at https://github.com/yuhoo0302/CUA-US.
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