SWDL: Stratum-Wise Difference Learning with Deep Laplacian Pyramid for Semi-Supervised 3D Intracranial Hemorrhage Segmentation
Journal:
arXiv
Published Date:
Jun 12, 2025
Abstract
Recent advances in medical imaging have established deep learning-based
segmentation as the predominant approach, though it typically requires large
amounts of manually annotated data. However, obtaining annotations for
intracranial hemorrhage (ICH) remains particularly challenging due to the
tedious and costly labeling process. Semi-supervised learning (SSL) has emerged
as a promising solution to address the scarcity of labeled data, especially in
volumetric medical image segmentation. Unlike conventional SSL methods that
primarily focus on high-confidence pseudo-labels or consistency regularization,
we propose SWDL-Net, a novel SSL framework that exploits the complementary
advantages of Laplacian pyramid and deep convolutional upsampling. The
Laplacian pyramid excels at edge sharpening, while deep convolutions enhance
detail precision through flexible feature mapping. Our framework achieves
superior segmentation of lesion details and boundaries through a difference
learning mechanism that effectively integrates these complementary approaches.
Extensive experiments on a 271-case ICH dataset and public benchmarks
demonstrate that SWDL-Net outperforms current state-of-the-art methods in
scenarios with only 2% labeled data. Additional evaluations on the publicly
available Brain Hemorrhage Segmentation Dataset (BHSD) with 5% labeled data
further confirm the superiority of our approach. Code and data have been
released at https://github.com/SIAT-CT-LAB/SWDL.
