Attention-guided Quality Assessment for Automated Cryo-EM Grid Screening

• URL: http://arxiv.org/abs/2007.05593v2
• Date: Tue, 21 Jul 2020 21:55:17 GMT
• Title: Attention-guided Quality Assessment for Automated Cryo-EM Grid Screening
• Authors: Hong Xu, David E. Timm, Shireen Y. Elhabian
• Abstract summary: We propose the first deep learning framework, XCryoNet, for automated cryo-EM grid screening. XCryoNet is a semi-supervised, attention-guided deep learning approach that provides explainable scoring of automatically extracted square images. Results show up to 8% and 37% improvements over a fully supervised and a no-attention solution, respectively, when labeled data is scarce.
• Score: 7.213084307941148
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cryogenic electron microscopy (cryo-EM) has become an enabling technology in drug discovery and in understanding molecular bases of disease by producing near-atomic resolution (less than 0.4 nm) 3D reconstructions of biological macromolecules. The imaging process required for 3D reconstructions involves a highly iterative and empirical screening process, starting with the acquisition of low magnification images of the cryo-EM grids. These images are inspected for squares that are likely to contain useful molecular signals. Potentially useful squares within the grid are then imaged at progressively higher magnifications, with the goal of identifying sub-micron areas within circular holes (bounded by the squares) for imaging at high magnification. This arduous, multi-step data acquisition process represents a bottleneck for obtaining a high throughput data collection. Here, we focus on automating the early decision making for the microscope operator, scoring low magnification images of squares, and proposing the first deep learning framework, XCryoNet, for automated cryo-EM grid screening. XCryoNet is a semi-supervised, attention-guided deep learning approach that provides explainable scoring of automatically extracted square images using limited amounts of labeled data. Results show up to 8% and 37% improvements over a fully supervised and a no-attention solution, respectively, when labeled data is scarce.

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