HDC: Hierarchical Distillation for Multi-level Noisy Consistency in Semi-Supervised Fetal Ultrasound Segmentation
Journal:
arXiv
Published Date:
Apr 14, 2025
Abstract
Transvaginal ultrasound is a critical imaging modality for evaluating
cervical anatomy and detecting physiological changes. However, accurate
segmentation of cervical structures remains challenging due to low contrast,
shadow artifacts, and indistinct boundaries. While convolutional neural
networks (CNNs) have demonstrated efficacy in medical image segmentation, their
reliance on large-scale annotated datasets presents a significant limitation in
clinical ultrasound imaging. Semi-supervised learning (SSL) offers a potential
solution by utilizing unlabeled data, yet existing teacher-student frameworks
often encounter confirmation bias and high computational costs. In this paper,
a novel semi-supervised segmentation framework, called HDC, is proposed
incorporating adaptive consistency learning with a single-teacher architecture.
The framework introduces a hierarchical distillation mechanism with two
objectives: Correlation Guidance Loss for aligning feature representations and
Mutual Information Loss for stabilizing noisy student learning. The proposed
approach reduces model complexity while enhancing generalization. Experiments
on fetal ultrasound datasets, FUGC and PSFH, demonstrate competitive
performance with reduced computational overhead compared to multi-teacher
models.
