1. Enhancing representation in radiography-reports foundation model: a granular alignment algorithm using masked contrastive learning.
- Author
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Huang W, Li C, Zhou HY, Yang H, Liu J, Liang Y, Zheng H, Zhang S, and Wang S
- Subjects
- Humans, Radiography, Thoracic methods, Image Processing, Computer-Assisted methods, Machine Learning, Radiographic Image Interpretation, Computer-Assisted methods, Algorithms
- 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., (© 2024. The Author(s).)
- Published
- 2024
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