ArticleNature communications2024
Enhancing representation in radiography-reports foundation model: a granular alignment algorithm using masked contrastive learning.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed.
- A multimodal vision-language model for generalizable annotation-free pathology localization.Nature biomedical engineering · 2026Article
- Transparent chest radiograph foundation model enables explainable human disease profiling.NPJ digital medicine · 2026Article
- Prediction of severe pediatric community-acquired pneumonia using multimodal fusion of chest radiographs and clinical data.BMC pediatrics · 2026Article
- Medical Vision-Language Models: Existing Technologies, Clinical Applications and Future Directions.Sensors (Basel, Switzerland) · 2026Review
- A robust vision language model for molecular status prediction and radiology report generation in adult-type diffuse gliomas.NPJ digital medicine · 2026Article
- Foundation models for X-ray interpretation: a narrative review of current techniques and future perspectives in diagnostic imaging.Quantitative imaging in medicine and surgery · 2026Review
- A generalizable foundation model for analysis of human brain MRI.Nature neuroscience · 2026Article
- A Systematic Review of Contrastive Learning in Medical AI: Foundations, Biomedical Modalities, and Future Directions.Bioengineering (Basel, Switzerland) · 2026Review
- Foundation Models Meet Medical Image Interpretation.Research (Washington, D.C.) · 2026Review
- A lung CT vision foundation model facilitating disease diagnosis and medical imaging.Nature communications · 2025Article
- The Application of Foundation Models in Radiology: Bridging Images, Reports, and Beyond.Journal of the Korean Society of Radiology · 2025Review
- Quantitative assessment of impact of technical and population-based factors on fairness of AI models for chest X-ray scans.Computers in biology and medicine · 2025Article
- ECSA: Mitigating Catastrophic Forgetting and Few-Shot Generalization in Medical Visual Question Answering.Tomography (Ann Arbor, Mich.) · 2025Article
- EchoFM: Foundation Model for Generalizable Echocardiogram Analysis.IEEE transactions on medical imaging · 2025Article
- Multimodal Large Language Models in Medical Imaging: Current State and Future Directions.Korean journal of radiology · 2025Review
- From large language models to multimodal AI: a scoping review on the potential of generative AI in medicine.Biomedical engineering letters · 2025Review
- MetaGP: A generative foundation model integrating electronic health records and multimodal imaging for addressing unmet clinical needs.Cell reports. Medicine · 2025Article
- Rethinking Domain-Specific Pretraining by Supervised or Self-Supervised Learning for Chest Radiograph Classification: A Comparative Study Against ImageNet Counterparts in Cold-Start Active Learning.Health care science · 2025Article
- Generative Artificial Intelligence in Medical Imaging: Foundations, Progress, and Clinical Translation.Research (Washington, D.C.) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Recently, multi-modal vision-language foundation models have gained significant attention in the medical field. While these models offer great opportunities, they still face crucial challenges, such as the requirement for fine-grained knowledge understanding in computer-aided diagnosis and the capability of utilizing very limited or even no task-specific labeled data in real-world clinical applications. In this study, we present MaCo, a masked contrastive chest X-ray foundation model that tackles these challenges. MaCo explores masked contrastive learning to simultaneously achieve fine-grained image understanding and zero-shot learning for a variety of medical imaging tasks. It designs a correlation weighting mechanism to adjust the correlation between masked chest X-ray image patches and their corresponding reports, thereby enhancing the model's representation learning capabilities. To evaluate the performance of MaCo, we conducted extensive experiments using 6 well-known open-source X-ray datasets. The experimental results demonstrate the superiority of MaCo over 10 state-of-the-art approaches across tasks such as classification, segmentation, detection, and phrase grounding. These findings highlight the significant potential of MaCo in advancing a wide range of medical image analysis tasks.
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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.