ArticleFrontiers in molecular biosciences2020
One-Shot Learning With Attention-Guided Segmentation in Cryo-Electron Tomography.
Article in Frontiers in molecular biosciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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Who cites it
10 citing papers in PubMed.
- From Unsupervised to Zero-Shot: 3D Domain Adaptation for Cryo-Electron Tomography Segmentation.International journal of computer vision · 2026Article
- AITom: AI-guided cryo-electron tomography image analyses toolkit.Journal of structural biology · 2025Article
- An image processing pipeline for electron cryo-tomography in RELION-5.FEBS open bio · 2024Article
- CryoSAM: Training-free CryoET Tomogram Segmentation with Foundation Models.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
- Towards full-stack deep learning-empowered data processing pipeline for synchrotron tomography experiments.Innovation (Cambridge (Mass.)) · 2024Review
- Computational methods forFrontiers in cellular and infection microbiology · 2023Review
- Spaghetti Tracer: A Framework for Tracing Semiregular Filamentous Densities in 3D Tomograms.Biomolecules · 2022Article
- Challenges and triumphs in cryo-electron tomography.iScience · 2021Review
- Limited View Tomographic Reconstruction Using a Cascaded Residual Dense Spatial-Channel Attention Network With Projection Data Fidelity Layer.IEEE transactions on medical imaging · 2021Article
- Detecting Protein Communities in Native Cell Extracts by Machine Learning: A Structural Biologist's Perspective.Frontiers in molecular biosciences · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Cryo-electron Tomography (cryo-ET) generates 3D visualization of cellular organization that allows biologists to analyze cellular structures in a near-native state with nano resolution. Recently, deep learning methods have demonstrated promising performance in classification and segmentation of macromolecule structures captured by cryo-ET, but training individual deep learning models requires large amounts of manually labeled and segmented data from previously observed classes. To perform classification and segmentation in the wild (i.e., with limited training data and with unseen classes), novel deep learning model needs to be developed to classify and segment unseen macromolecules captured by cryo-ET. In this paper, we develop a one-shot learning framework, called cryo-ET one-shot network (COS-Net), for simultaneous classification of macromolecular structure and generation of the voxel-level 3D segmentation, using only one training sample per class. Our experimental results on 22 macromolecule classes demonstrated that our COS-Net could efficiently classify macromolecular structures with small amounts of samples and produce accurate 3D segmentation at the same time.
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Registered trials
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