用半监督学习高效精准定位冷冻电镜中的蛋白质颗粒
SemiETPicker: Fast and Label-Efficient Particle Picking for CryoET Tomography Using Semi-Supervised Learning
- 基于热图监督的端到端检测模型，模仿关键点识别思路
- 在标签极少情况下，F1得分比纯监督方法高10%
- 适合缺乏标注数据的冷冻电镜研究者快速分析海量未标注数据
冷冻电镜断层成像（CryoET）结合子体积平均技术（SVA）是目前唯一能在分子分辨率下解析细胞内蛋白质结构的成像手段。粒子拾取任务——即在三维冷冻电镜体积中定位和分类目标蛋白质——仍是主要瓶颈。由于依赖耗时的人工标注，大量未标注断层图数据尚未被利用。本文提出一种快速、标签高效的半监督框架，充分利用这些未标注数据。该框架包含两部分：(i) 受关键点检测启发的端到端热图监督检测模型；(ii) 教师-学生协同训练机制，在标签稀疏条件下提升性能。此外，引入多视角伪标签和专为冷冻电镜设计的DropBlock增强策略进一步提升效果。在大规模CZII数据集上的大量实验表明，本方法相比监督基线将F1值提升10%，验证了半监督学习在挖掘未标注冷冻电镜数据方面的巨大潜力。
原文摘要 · Abstract (English)
Cryogenic Electron Tomography (CryoET) combined with sub-volume averaging (SVA) is the only imaging modality capable of resolving protein structures inside cells at molecular resolution. Particle picking, the task of localizing and classifying target proteins in 3D CryoET volumes, remains the main bottleneck. Due to the reliance on time-consuming manual labels, the vast reserve of unlabeled tomograms remains underutilized. In this work, we present a fast, label-efficient semi-supervised framework that exploits this untapped data. Our framework consists of two components: (i) an end-to-end heatmap-supervised detection model inspired by keypoint detection, and (ii) a teacher-student co-training mechanism that enhances performance under sparse labeling conditions. Furthermore, we introduce multi-view pseudo-labeling and a CryoET-specific DropBlock augmentation strategy to further boost performance. Extensive evaluations on the large-scale CZII dataset show that our approach improves F1 by 10% over supervised baselines, underscoring the promise of semi-supervised learning for leveraging unlabeled CryoET data.
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