Enhancing Cervical Segmentation via Alternate Diverse Teaching in Semi-Supervised Learning

In clinical practice, the cervical structure plays a critical role in predicting preterm birth, thus making accurate segmentation of this structure in ultrasound images of paramount importance. However, the performance of existing deep learning methods is limited by the high cost of labeling ultrasound images. In this context, semi-supervised medical image segmentation techniques have emerged as promising solutions, particularly recent teacher-student frameworks that have demonstrated notable efficacy. Despite their success, these methods are often subject to confirmation bias, which undermines their reliability. To address this issue, we introduce a novel approach, Alternate Diverse Teaching (AD-MT), for the semi-supervised segmentation of cervical ultrasound images. AD-MT enhances model performance by alternately updating two teacher models, offering diverse perspectives during learning. Furthermore, a conftict-combating module is incorporated to ensure that the model learns from the discrepancies between teacher predictions. This methodology effectively harnesses both labeled and unlabeled data for segmentation tasks. Experimental results demonstrate that AD-MT achieves competitive segmentation performance, as evidenced by its strong showing in the ISBI 2025 Fetal Ultrasound Grand Challenge.