Large-Scale 3D Medical Image Pre-Training With Geometric Context Priors

作者：Linshan Wu, Jiaxin Zhuang, Hao Chen · 发表于：IEEE Transactions on Pattern Analysis and Machine Intelligence · 年份：2025 · DOI：10.1109/tpami.2025.3639593 · 被引用次数：7 · 研究领域：Advanced Neural Network Applications、COVID-19 diagnosis using AI、AI in cancer detection

The scarcity of annotations poses a significant challenge in medical image analysis, which demands extensive efforts from radiologists, especially for high-dimension 3D medical images. Large-scale pre-training has emerged as a promising label-efficient solution, owing to the utilization of large-scale data, large models, and advanced pre-training techniques. However, its development in medical images remains underexplored. The primary challenge lies in harnessing large-scale unlabeled data and learning high-level semantics without annotations. We observe that 3D medical images exhibit consistent geometric context, i.e., consistent geometric relations between different organs, which leads to a promising way for learning consistent representations. Motivated by this, we introduce a simple-yet-effective Volume Contrast (VoCo) framework to leverage geometric context priors for self-supervision. Given an input volume, we extract base crops from different regions to construct positive and negative pairs for contrastive learning. Then we predict the contextual position of a random crop by contrasting its similarity to the base crops. In this way, VoCo implicitly encodes the inherent geometric context into model representations, facilitating high-level semantic learning without annotations. To assess effectiveness, we (1) introduce PreCT-160 K, the largest medical image pre-training dataset to date, which comprises 160 K Computed Tomography (CT) volumes covering diverse anatomic stru...