The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Multi-Modal Understanding and Generation for Medical Images and Text via Vision-Language Pre-Training
Open accessAbstract
Recently a number of studies demonstrated impressive performance on diverse vision-language multi-modal tasks such as image captioning and visual question answering by extending the BERT architecture with multi-modal pre-training objectives. In this work we explore a broad set of multi-modal representation learning tasks in the medical domain, specifically using radiology images and the unstructured report. We propose Medical Vision Language Learner (MedViLL), which adopts a BERT-based architecture combined with a novel multi-modal attention masking scheme to maximize generalization performance for both vision-language understanding tasks (diagnosis classification, medical image-report retrieval, medical visual question answering) and vision-language generation task (radiology report generation). By statistically and rigorously evaluating the proposed model on four downstream tasks with three radiographic image-report datasets (MIMIC-CXR, Open-I, and VQA-RAD), we empirically demonstrate the superior downstream task performance of MedViLL against various baselines, including task-specific architectures.
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Cited by
Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing
Bannur, S., Hyland, S., Liu, Q., Pérez-García, F., Ilse, M., Castro, D. C., Boecking, B., Sharma, H., Bouzid, K., Thieme, A., et al. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01442 · Cited by 227
Clinical-BERT: Vision-Language Pre-training for Radiograph Diagnosis and Reports Generation
Yan, B., & Pei, M. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i3.20204 · Cited by 94
He, K., Mao, R., Lin, Q., Ruan, Y., Lan, X., Feng, M., & Cambria, E. (2024) · doi:10.2139/ssrn.4809363 · Cited by 69
A scoping review on multimodal deep learning in biomedical images and texts
Sun, Z., Lin, M., Zhu, Q., Xie, Q., Wang, F., Lu, Z., & Peng, Y. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104482 · Cited by 69
Utilizing Longitudinal Chest X-Rays and Reports to Pre-Fill Radiology Reports
Zhu, Q., Mathai, T. S., Mukherjee, P., Peng, Y., Summers, R. M., & Lu, Z. (2023) · arXiv · doi:10.48550/arxiv.2306.08749 · Cited by 36
Metadata
- Authors
- Moon, J. H., Lee, H., Shin, W., Kim, Y.-H., & Choi, E.
- Venue
- IEEE Journal of Biomedical and Health Informatics
- Published
- 2022-12
- Volume
- 26
- Issue
- 12
- Pages
- 6070-6080
- ISSN
- 2168-2194 (ISSN-L)
- 2168-2208
- OpenAlex
- W3164654615
- Cited by
- 212
- References
- 19
- FWCI
- 14.65
- Type
- journal article
- Language
- en
- Open access
- green
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- Moon, J. H., Lee, H., Shin, W., Kim, Y.-H., & Choi, E. (2022). Multi-Modal Understanding and Generation for Medical Images and Text via Vision-Language Pre-Training. IEEE Journal of Biomedical and Health Informatics. https://doi.org/10.1109/jbhi.2022.3207502
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