Few-Shot Classification of Cryo-EM Micrographs Using Triplet Loss Embeddings.

Area of Science:

• Structural Biology
• Biophysics
• Computational Biology

Background:

• Manual screening of cryo-electron microscopy (cryo-EM) micrographs is time-consuming and computationally expensive.
• Automated quality assessment is crucial for efficient cryo-EM data processing.
• Few-shot learning applications in cryo-EM are emerging but underexplored for micrograph-level quality assessment.

Purpose of the Study:

• To develop and evaluate a few-shot learning framework for automated cryo-EM micrograph quality assessment.
• To improve the efficiency and reduce the manual labeling burden in cryo-EM data processing.
• To explore the effectiveness of triplet loss embeddings for micrograph classification.

Main Methods:

• A framework combining few-shot learning with cryo-EM micrograph classification was developed.
• Triplet loss embeddings were utilized, integrating both real-space and Fourier-space information.
• The model was trained and validated on multiple EMPIAR datasets.

Main Results:

• The proposed method achieved competitive performance in micrograph classification with only 1-5 labeled examples per class.
• The triplet loss embedding approach outperformed traditional cross-entropy training in the few-shot scenario.
• The framework demonstrated robustness across different EMPIAR datasets.

Conclusions:

• Few-shot learning offers a practical solution for rapid adaptation of automated micrograph screening in cryo-EM.
• The developed framework significantly reduces the need for extensive manual labeling.
• This approach enhances the efficiency of cryo-EM data processing pipelines.