构建首个冷冻电镜断层扫描基础模型，提升低信噪比下结构分析的准确性和泛化能力。
Towards Foundation Models for Cryo-ET Subtomogram Analysis
- 用合成数据生成器CryoEngine构建90万+子断层图用于预训练
- 提出APT-ViT模型，在噪声和形变下仍保持高精度分类与对齐
- 设计抗噪对比学习策略，使模型在真实数据上表现优于现有方法
冷冻电子断层扫描（cryo-ET）可实现生物大分子在原位环境中的可视化，其中子断层图分析任务如分类、对齐与平均对结构解析至关重要。然而，有效分析受限于标注数据稀少、噪声严重及泛化能力差。为此，本文首次探索冷冻电镜断层扫描子断层图的基础模型。首先，提出CryoEngine，一个大规模合成数据生成器，从452类粒子生成超过90.4万张子断层图用于预训练；其次，设计自适应相位标记增强的视觉变换器（APT-ViT），通过自适应相位标记模块增强等变性，提升对几何与语义变化的鲁棒性；第三，引入抗噪对比学习（NRCL）策略，在严重噪声条件下稳定表示学习。在24个合成与真实数据集上的评估显示，该方法在三类主要子断层图任务中均达到当前最优性能，并展现出对未见数据集的强大泛化能力，推动了冷冻电镜断层扫描中可扩展、鲁棒的子断层图分析发展。
原文摘要 · Abstract (English)
Cryo-electron tomography (cryo-ET) enables in situ visualization of macromolecular structures, where subtomogram analysis tasks such as classification, alignment, and averaging are critical for structural determination. However, effective analysis is hindered by scarce annotations, severe noise, and poor generalization. To address these challenges, we take the first step towards foundation models for cryo-ET subtomograms. First, we introduce CryoEngine, a large-scale synthetic data generator that produces over 904k subtomograms from 452 particle classes for pretraining. Second, we design an Adaptive Phase Tokenization-enhanced Vision Transformer (APT-ViT), which incorporates adaptive phase tokenization as an equivariance-enhancing module that improves robustness to both geometric and semantic variations. Third, we introduce a Noise-Resilient Contrastive Learning (NRCL) strategy to stabilize representation learning under severe noise conditions. Evaluations across 24 synthetic and real datasets demonstrate state-of-the-art (SOTA) performance on all three major subtomogram tasks and strong generalization to unseen datasets, advancing scalable and robust subtomogram analysis in cryo-ET.
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