ArticleProceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence2021
Context Matters: Graph-based Self-supervised Representation Learning for Medical Images.
Article in Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- High-dimensional causal mediation analysis by partial sum statistic and sample splitting strategy in imaging genetics application.Bioinformatics (Oxford, England) · 2025Article
- Deep learning integration of chest computed tomography and plasma proteomics to identify novel aspects of severe COVID-19 pneumonia.Journal of intensive medicine · 2025Article
- Self-supervised learning framework application for medical image analysis: a review and summary.Biomedical engineering online · 2024Review
- AntiFormer: graph enhanced large language model for binding affinity prediction.Briefings in bioinformatics · 2024Article
- Worldwide research landscape of artificial intelligence in lung disease: A scientometric study.Heliyon · 2024Article
- DrasCLR: A self-supervised framework of learning disease-related and anatomy-specific representation for 3D lung CT images.Medical image analysis · 2024Article
- Deep Learning Integration of Chest Computed Tomography Imaging and Gene Expression Identifies Novel Aspects of COPD.Chronic obstructive pulmonary diseases (Miami, Fla.) · 2023Article
- Self-supervised learning for medical image classification: a systematic review and implementation guidelines.NPJ digital medicine · 2023Review
- Reverse translation of artificial intelligence in glaucoma: Connecting basic science with clinical applications.Frontiers in ophthalmology · 2023Article
- Can contrastive learning avoid shortcut solutions?Advances in neural information processing systems · 2021Article
- Extracting Disease-Relevant Features with Adversarial Regularization.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Supervised learning method requires a large volume of annotated datasets. Collecting such datasets is time-consuming and expensive. Until now, very few annotated COVID-19 imaging datasets are available. Although self-supervised learning enables us to bootstrap the training by exploiting unlabeled data, the generic self-supervised methods for natural images do not sufficiently incorporate the
Identifiers
34650825PMC8513790What 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.