Bridging the Synthetic-to-Real Gap for Few-Shot Cryo-ET Classification

2026-09-12

AI Summary

为解决冷冻电镜断层扫描中子断层图分类因标注样本稀缺而面临的难题，提出了一种新的合成到真实的适应框架，通过可学习的转换模块在输入和特征层面缩小合成与真实数据间的差距。

Abstract

Subtomogram classification in cryo-electron tomography (cryo-ET) is a challenging problem due to the scarcity of labeled examples. While cryo-ET simulators can be adopted to generate unlimited synthetic data, the substantial domain gap between synthetic and real subtomograms hinders its practical utilization. In this work, we propose a novel synthetic-to-real adaptation framework with a learnable transformation module, bridging this gap at both the input and feature levels. Extensive experiments demonstrate that our method consistently outperforms existing transfer learning baselines in few-shot settings.

Problem

• cryo-ET
• subtomogram classification
• synthetic-to-real gap
• labeled examples scarcity

Innovation

• synthetic-to-real adaptation
• learnable transformation module
• few-shot learning