Multi-task paired masking with alignment modeling for medical vision-language pre-training
Open accessAbstract
In recent years, the growing demand for medical imaging diagnosis has placed a significant burden on radiologists. As a solution, Medical Vision-Language Pre-training (Med-VLP) methods have been proposed to learn universal representations from medical images and reports, benefiting downstream tasks without requiring fine-grained annotations. However, existing methods have overlooked the importance of cross-modal alignment in joint image-text reconstruction, resulting in insufficient cross-modal interaction. To address this limitation, we propose a unified Med-VLP framework based on Multi-task Paired Masking with Alignment (MPMA) to integrate the cross-modal alignment task into the joint image-text reconstruction framework to achieve more comprehensive cross-modal interaction, while a Global and Local Alignment (GLA) module is designed to assist self-supervised paradigm in obtaining semantic representations with rich domain knowledge. Furthermore, we introduce a Memory-Augmented Cross-Modal Fusion (MA-CMF) module to fully integrate visual information to assist report reconstruction and fuse the multi-modal representations adequately. Experimental results demonstrate that the proposed unified approach outperforms previous methods in all downstream tasks, including uni-modal, cross-modal, and multi-modal tasks.
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During training, specialized encoders extract features from different modalities, then cross-modal alignment mechanisms integrate them.
“During training, the model employs specialized encoders (e.g., convolutional neural networks for visual data, transformer architectures for text and audio data) [171,172] to extract features from different modalities, and then interacts and integrates these features through cross-modal alignment mechanisms [173,174].”
From Multimodal fusion and vision–language models: A survey for robot vision (2026) · cites this paper as [174]
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Abnormal-region-aware Multi-modal Feature Fusion for medical report generation
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R2GenCSR: Mining Contextual and Residual Information for LLMs-Based Radiology Report Generation
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Guided Masked Self-Distillation Modeling for Distributed Multimedia Sensor Event Analysis
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Multi-Grained Vision-and-Language Model for Medical Image and Text Alignment
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Hybrid fusion framework for multimodal data integration
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Vision-Language Pre-training from Synthetic Data
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Progressive Learning of Instance-Level Proxy Semantics for Few-Shot Action Recognition
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CyclicAligner: Knowledge-Enhanced Cyclical Alignment for Chest X-Ray Report Generation
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Medical Transformer With Mix Mask Generation for Thorax Disease Classification
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- Authors
- Zhang, K., Yang, Y., Yu, J., Jiang, H., Fan, J., Huang, Q., & Han, W.
- Affiliations
- Hangzhou Dianzi University (CN)
- Sir Run Run Shaw Hospital (CN)
- AI Lab at Lenovo Research, China
- University of Chinese Academy of Sciences (CN)
- Zhejiang Normal University (CN)
- Venue
- IEEE Transactions on Multimedia
- Published
- 2024
- Volume
- 26
- Pages
- 4706-4721
- ISSN
- 1520-9210 (ISSN-L)
- 1941-0077
- OpenAlex
- W4387789891
- Cited by
- 51
- References
- 63
- Citations by year
- 2026: 10 · 2025: 30 · 2024: 11
- FWCI
- 4.63
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Multimedia (publishedVersion)
- arXiv (Cornell University) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (62125201, 62020106007, 62176230)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- cross-modal alignment, self-supervised learning, radiology reports
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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