Anatomy-Guided Self-Supervised Distillation Learning for Medical Image Analysis

3-D medical imaging modalities, including CT and MRI, provide high-resolution views essential for precision medicine. However, the increasing volume and complexity of 3-D medical images challenge manual analysis, particularly in classification and segmentation tasks. Although deep learning has shown considerable promise, it struggles to characterize small-scale, low-contrast anatomical structures, generalize across imaging domains, and mitigate annotation scarcity. Self-supervised learning (SSL) has emerged as an effective and annotation-efficient solution, yet existing methods, largely adapted from natural images, often fail to capture the anatomical heterogeneity and complex semantic dependencies inherent in 3-D medical data. To address these limitations, we propose AG-SSD (Anatomy-Guided Self-Supervised Distillation), a framework that explicitly incorporates anatomical priors into SSL. AG-SSD comprises three complementary modules: 1) cross-view anatomical consistency (CVAC), which generates multi-scale, anatomically consistent positive pairs via overlap-aware cropping; 2) edge-aware adaptive masking (EAAM), which prioritizes anatomy-sensitive, high-edge regions to enhance local feature learning and robust global representation; and 3) cross-view attention alignment (CVAA), which leverages attention-based fusion to achieve semantic compensation and alignment across views, mitigating semantic drift to stabilize distillation. These modules are optimized using a unified objective that combines intra-view patch distillation, inter-view [CLS] token distillation, and masked patch reconstruction. Extensive experiments on CT and MRI datasets demonstrate that AG-SSD consistently outperforms state-of-the-art SSL methods in both classification and segmentation under annotation-scarce scenarios, highlighting its potential as a scalable, label-efficient paradigm for 3-D medical image analysis and clinical applications.