ArticleJournal of structural biology2024
Miffi: Improving the accuracy of CNN-based cryo-EM micrograph filtering with fine-tuning and Fourier space information.
Article in Journal of structural biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- An unattended image-processing pipeline for on-the-fly quality assessment and 3D exploration in cryo-EM.Acta crystallographica. Section D, Structural biology · 2026Article
- High-resolution cryo-EM structure of integrin αIIbβ3 bound to disease-causing maternal HPA-1a antibody that blocks integrin activation.Science advances · 2026Article
- prismPYP: Power-spectrum and image domain learning for self-supervised micrograph evaluation.Structure (London, England : 1993) · 2026Article
- Few-Shot Classification of Cryo-EM Micrographs Using Triplet Loss Embeddings.bioRxiv : the preprint server for biology · 2025Article
- A large-scale curated and filterable dataset for cryo-EM foundation model pre-training.Scientific data · 2025Article
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2 authors.
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Abstract
Efficient and high-accuracy filtering of cryo-electron microscopy (cryo-EM) micrographs is an emerging challenge with the growing speed of data collection and sizes of datasets. Convolutional neural networks (CNNs) are machine learning models that have been proven successful in many computer vision tasks, and have been previously applied to cryo-EM micrograph filtering. In this work, we demonstrate that two strategies, fine-tuning models from pretrained weights and including the power spectrum of micrographs as input, can greatly improve the attainable prediction accuracy of CNN models. The resulting software package, Miffi, is open-source and freely available for public use (https://github.com/ando-lab/miffi).
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