ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention2020
Joint Modeling of Chest Radiographs and Radiology Reports for Pulmonary Edema Assessment.
Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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
21 citing papers in PubMed.
- Learning like a radiologist: a medical vision-language model for radiological image analysis via curriculum learning.NPJ digital medicine · 2026Article
- Contrastive report and multiparametric dual-region magnetic resonance imaging learning for the preoperative prediction of axillary lymph node metastasis in breast cancer.Quantitative imaging in medicine and surgery · 2026Article
- Advancements in Medical Radiology Through Multimodal Machine Learning: A Comprehensive Overview.Bioengineering (Basel, Switzerland) · 2025Review
- Review of multimodal machine learning approaches in healthcare.An international journal on information fusion · 2025Article
- Generalizable self-supervised learning for brain CTA in acute stroke.Computers in biology and medicine · 2025Article
- Boosting Deep Learning for Interpretable Brain MRI Lesion Detection through the Integration of Radiology Report Information.Radiology. Artificial intelligence · 2024Article
- Explainable AI to identify radiographic features of pulmonary edema.Radiology advances · 2024Article
- Multimodal Machine Learning in Image-Based and Clinical Biomedicine: Survey and Prospects.International journal of computer vision · 2024Article
- Beyond images: an integrative multi-modal approach to chest x-ray report generation.Frontiers in radiology · 2024Article
- Sample-Specific Debiasing for Better Image-Text Models.Proceedings of machine learning research · 2023Article
- Using Multiple Instance Learning to Build Multimodal Representations.Information processing in medical imaging : proceedings of the ... conference · 2023Article
- Deep multimodal fusion of image and non-image data in disease diagnosis and prognosis: a review.Progress in biomedical engineering (Bristol, England) · 2023Article
- SELF-SUPERVISED LEARNING WITH RADIOLOGY REPORTS, A COMPARATIVE ANALYSIS OF STRATEGIES FOR LARGE VESSEL OCCLUSION AND BRAIN CTA IMAGES.Proceedings. IEEE International Symposium on Biomedical Imaging · 2023Article
- Event-Based Clinical Finding Extraction from Radiology Reports with Pre-trained Language Model.Journal of digital imaging · 2023Article
- Local Contrastive Learning for Medical Image Recognition.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- A Survey on AI Techniques for Thoracic Diseases Diagnosis Using Medical Images.Diagnostics (Basel, Switzerland) · 2022Review
- That's the Wrong Lung! Evaluating and Improving the Interpretability of Unsupervised Multimodal Encoders for Medical Data.Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing · 2022Article
- Predicting Osteoarthritis of the Temporomandibular Joint Using Random Forest with Privileged Information.Ethical and philosophical issues in medical imaging, multimodal learning and fusion across scales for clinical decision support, and topological data analysis for biomedical imaging : 1st International Workshop, EPIMI 2022, 12th Interna... · 2022Article
- Few-Shot Learning Geometric Ensemble for Multi-label Classification of Chest X-Rays.Data augmentation, labelling, and imperfections : second MICCAI workshop, DALI 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022, proceedings. DALI (Workshop) (2nd : 2022 : Singapore) · 2022Article
- Improving Joint Learning of Chest X-Ray and Radiology Report by Word Region Alignment.Machine learning in medical imaging. MLMI (Workshop) · 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
9 authors.
Funding
Abstract
We propose and demonstrate a novel machine learning algorithm that assesses pulmonary edema severity from chest radiographs. While large publicly available datasets of chest radiographs and free-text radiology reports exist, only limited numerical edema severity labels can be extracted from radiology reports. This is a significant challenge in learning such models for image classification. To take advantage of the rich information present in the radiology reports, we develop a neural network model that is trained on both images and free-text to assess pulmonary edema severity from chest radiographs at inference time. Our experimental results suggest that the joint image-text representation learning improves the performance of pulmonary edema assessment compared to a supervised model trained on images only. We also show the use of the text for explaining the image classification by the joint model. To the best of our knowledge, our approach is the first to leverage free-text radiology reports for improving the image model performance in this application. Our code is available at: https://github.com/RayRuizhiLiao/joint_chestxray.
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.