ArticleFrontiers in big data2020
Reducing Annotation Burden Through Multimodal Learning.
Article in Frontiers in big data, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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.
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Who cites it
8 citing papers in PubMed.
- Evaluating multiple large language models on orbital diseases.Frontiers in cell and developmental biology · 2025Article
- Benefits and Risks of AI in Health Care: Narrative Review.Interactive journal of medical research · 2024Review
- Federated learning for multi-omics: A performance evaluation in Parkinson's disease.Patterns (New York, N.Y.) · 2024Article
- Federated Learning for multi-omics: a performance evaluation in Parkinson's disease.bioRxiv : the preprint server for biology · 2024Article
- Progress in the application of artificial intelligence in skin wound assessment and prediction of healing time.American journal of translational research · 2024Review
- Multi-modality machine learning predicting Parkinson's disease.NPJ Parkinson's disease · 2022Article
- Harnessing multimodal data integration to advance precision oncology.Nature reviews. Cancer · 2022Review
- A Deep Learning Approach to Diagnostic Classification of Prostate Cancer Using Pathology-Radiology Fusion.Journal of magnetic resonance imaging : JMRI · 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
5 authors.
Funding
Abstract
Choosing an optimal data fusion technique is essential when performing machine learning with multimodal data. In this study, we examined deep learning-based multimodal fusion techniques for the combined classification of radiological images and associated text reports. In our analysis, we (1) compared the classification performance of three prototypical multimodal fusion techniques:
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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.