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Improving Medical Visual Representation Learning With Pathological-Level Cross-Modal Alignment and Correlation
IEEE Journal of Biomedical and Health Informatics
|October 23, 2025
Summary
This study introduces PLACE, a framework for medical image and report analysis. It enhances understanding of pathology by aligning visual and textual data at a detailed level, improving performance on various medical tasks.
Area of Science:
- Medical imaging analysis
- Natural Language Processing
- Machine Learning
Background:
- Joint learning from medical image-report pairs is crucial for transferring knowledge to downstream tasks.
- Prior methods often focus on instance or token-level alignment, overlooking pathology-level consistency.
- Developing methods for robust medical visual representation learning is an active research area.
Purpose of the Study:
- To present a novel framework, PLACE, for pathological-level alignment and fine-grained detail enrichment in medical image-report learning.
- To improve the consistency of pathology observations between medical images and their corresponding reports.
- To enhance the generalizability and robustness of medical visual representation learning without requiring external annotations.
Main Methods:
- Proposed a pathological-level cross-modal alignment (PCMA) approach to maximize consistency between visual and textual pathology observations.
- Introduced a Visual Pathology Observation Extractor to derive representations from localized image tokens.
- Developed a proxy task for correlation exploration among image patches to enrich fine-grained details.
Main Results:
- The PLACE framework achieved state-of-the-art performance across multiple downstream tasks.
- Demonstrated significant improvements in classification, image-to-text retrieval, semantic segmentation, object detection, and report generation.
- The PCMA module showed effectiveness and robustness, independent of external disease annotations.
Conclusions:
- The proposed PLACE framework effectively enhances medical visual representation learning by focusing on pathological-level consistency.
- Correlation exploration enriches fine-grained details, leading to superior performance on diverse medical applications.
- This approach offers a robust and generalizable method for joint learning from medical image-report pairs.
