ArticleProceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition2020
Gum-Net: Unsupervised Geometric Matching for Fast and Accurate 3D Subtomogram Image Alignment and Averaging.
Article in Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- From Unsupervised to Zero-Shot: 3D Domain Adaptation for Cryo-Electron Tomography Segmentation.International journal of computer vision · 2026Article
- MVGFormer: Multi-view perspective with graph-guided transformer for cryo-ET segmentation.Knowledge-based systems · 2026Article
- AITom: AI-guided cryo-electron tomography image analyses toolkit.Journal of structural biology · 2025Article
- A noise-robust classification method for cryo-ET subtomograms with out-of-distribution detection.Bioinformatics (Oxford, England) · 2025Article
- Few-shot classification of Cryo-ET subvolumes with deep Brownian distance covariance.Briefings in bioinformatics · 2024Article
- DUAL: deep unsupervised simultaneous simulation and denoising for cryo-electron tomography.bioRxiv : the preprint server for biology · 2024Article
- High-throughput cryo-ET structural pattern mining by unsupervised deep iterative subtomogram clustering.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Computational methods forFrontiers in cellular and infection microbiology · 2023Review
- Harmony: A Generic Unsupervised Approach for Disentangling Semantic Content from Parameterized Transformations.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2022Article
- End-to-end robust joint unsupervised image alignment and clustering.Proceedings. IEEE International Conference on Computer Vision · 2021Article
- Weakly Supervised 3D Semantic Segmentation Using Cross-Image Consensus and Inter-Voxel Affinity Relations.Proceedings. IEEE International Conference on Computer Vision · 2021Article
- A unified framework for packing deformable and non-deformable subcellular structures in crowded cryo-electron tomogram simulation.BMC bioinformatics · 2020Article
- One-Shot Learning With Attention-Guided Segmentation in Cryo-Electron Tomography.Frontiers in molecular biosciences · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
We propose a Geometric unsupervised matching Network (Gum-Net) for finding the geometric correspondence between two images with application to 3D subtomogram alignment and averaging. Subtomogram alignment is the most important task in cryo-electron tomography (cryo-ET), a revolutionary 3D imaging technique for visualizing the molecular organization of unperturbed cellular landscapes in single cells. However, subtomogram alignment and averaging are very challenging due to severe imaging limits such as noise and missing wedge effects. We introduce an end-to-end trainable architecture with three novel modules specifically designed for preserving feature spatial information and propagating feature matching information. The training is performed in a fully unsupervised fashion to optimize a matching metric. No ground truth transformation information nor category-level or instance-level matching supervision information is needed. After systematic assessments on six real and nine simulated datasets, we demonstrate that Gum-Net reduced the alignment error by 40 to 50% and improved the averaging resolution by 10%. Gum-Net also achieved 70 to 110 times speedup in practice with GPU acceleration compared to state-of-the-art subtomogram alignment methods. Our work is the first 3D unsupervised geometric matching method for images of strong transformation variation and high noise level. The training code, trained model, and datasets are available in our open-source software AITom.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.