ArticleCell reports methods2026
Convpaint-Interactive pixel classification using pretrained neural networks.
Article in Cell reports methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Article
- Transcranial microtesla magnetic fields suppress neuroinflammation and neuronal oxidative stress burden.iScience · 2026Article
- A steady-state pool of calcium-dependent actin is maintained by Homer and controls epithelial mechanosensation.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present Convpaint, a universal computational framework for interactive pixel classification. Convpaint uses pretrained convolutional neural networks (CNNs), vision transformers (ViTs), or classical filter banks for feature extraction in combination with fast-to-train machine learning (ML) classifiers to enable easy segmentation across a wide variety of tasks. By integrating ViT-based features, Convpaint extends traditional pixel classification to image domains that require rich semantic understanding. Convpaint's modular design allows users to rapidly switch between feature extractors, balancing speed, spatial accuracy, and semantic depth based on the specific dataset. Available within the Python-based napari software ecosystem, Convpaint integrates seamlessly with other plugins into image processing pipelines, which we demonstrate with example workflows across different data modalities, from subcellular to cellular to animal scale.
Indexed as
Identifiers
What OpenQuestion holds
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.