ArticleIEEE journal of biomedical and health informatics2022
Contrastive Cross-Modal Pre-Training: A General Strategy for Small Sample Medical Imaging.
Article in IEEE journal of biomedical and health informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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
5 citing papers in PubMed.
- A transfer learning-based hybrid deep- and machine-learning regression approach for predicting the postoperative pulmonary function.European radiology experimental · 2026Article
- Medical Vision-Language Models: Existing Technologies, Clinical Applications and Future Directions.Sensors (Basel, Switzerland) · 2026Review
- A Systematic Review and Implementation Guidelines of Multimodal Foundation Models in Medical Imaging.Research square · 2025Article
- Development of CNN models for the enteral feeding tube positioning assessment on a small scale data set.BMC medical imaging · 2022Article
- Lung Disease Classification in CXR Images Using Hybrid Inception-ResNet-v2 Model and Edge Computing.Journal of healthcare engineering · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
A key challenge in training neural networks for a given medical imaging task is the difficulty of obtaining a sufficient number of manually labeled examples. In contrast, textual imaging reports are often readily available in medical records and contain rich but unstructured interpretations written by experts as part of standard clinical practice. We propose using these textual reports as a form of weak supervision to improve the image interpretation performance of a neural network without requiring additional manually labeled examples. We use an image-text matching task to train a feature extractor and then fine-tune it in a transfer learning setting for a supervised task using a small labeled dataset. The end result is a neural network that automatically interprets imagery without requiring textual reports during inference. We evaluate our method on three classification tasks and find consistent performance improvements, reducing the need for labeled data by 67%-98%.
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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.