Uncertainty-aware Diffusion and Reinforcement Learning for Joint Plane Localization and Anomaly Diagnosis in 3D Ultrasound
Journal:
arXiv
Published Date:
Jun 30, 2025
Abstract
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.
