3DME: Dual-Branch Encoder with Progressive Masking for 3D Medical Foundation Encoding Model

Abstract

Three-dimensional (3D) medical image analysis faces challenges such as massive data volume, difficulty in integrating cross-slice information, and limited model generalization. This paper proposes 3DME, a foundational model for 3D medical imaging. Its core innovations feature a dual-branch 3D encoder that integrates a Vision Transformer for modeling global long-range dependencies and a 3D graph convolutional network for capturing local voxel structures, enhanced by multi-level deformable attention for cross-planar correlation; a progressive volumetric masking strategy for self-supervised pretraining, which dynamically adjusts masking ratios and block sizes to force the model to learn cross-slice continuity and global semantics; and a unified foundation model framework supporting lightweight adaptation for downstream tasks. Experiments demonstrate that 3DME achieves state-of-the-art (SOTA) performance on 12 segmentation and classification tasks, exhibiting strong zero-shot transfer capabilities, thereby significantly enhancing model generalization and clinical deployment efficiency.