ArticleComputer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision2020
Two Stream Active Query Suggestion for Active Learning in Connectomics.
Article in Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A distribution-aware semi-supervised pipeline for cost-effective neuron segmentation.iScience · 2026Article
- A connectomic resource for neural cataloguing and circuit dissection of the larval zebrafish brain.bioRxiv : the preprint server for biology · 2025Article
- WASPSYN: A Challenge for Domain Adaptive Synapse Detection in Microwasp Brain Connectomes.IEEE transactions on medical imaging · 2024Article
- Multi-layered maps of neuropil with segmentation-guided contrastive learning.Nature methods · 2023Article
- Learning the heterogeneous representation of brain's structure from serial SEM images using a masked autoencoder.Frontiers in neuroinformatics · 2023Article
- Joint reconstruction of neuron and ultrastructure via connectivity consensus in electron microscope volumes.BMC bioinformatics · 2022Article
- Edge-colored directed subgraph enumeration on the connectome.Scientific reports · 2022Article
- Electron Microscopy Techniques for Investigating Structure and Composition of Hair-Cell Stereociliary Bundles.Frontiers in cell and developmental biology · 2021Article
Corrections and comments
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Authors and funding
16 authors.
Funding
Abstract
For large-scale vision tasks in biomedical images, the labeled data is often limited to train effective deep models. Active learning is a common solution, where a query suggestion method selects representative unlabeled samples for annotation, and the new labels are used to improve the base model. However, most query suggestion models optimize their learnable parameters only on the limited labeled data and consequently become less effective for the more challenging unlabeled data. To tackle this, we propose a
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Registered trials
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.