PRIOR: Prototype Representation Joint Learning from Medical Images and Reports (2307.12577v3)

Abstract: Contrastive learning based vision-language joint pre-training has emerged as a successful representation learning strategy. In this paper, we present a prototype representation learning framework incorporating both global and local alignment between medical images and reports. In contrast to standard global multi-modality alignment methods, we employ a local alignment module for fine-grained representation. Furthermore, a cross-modality conditional reconstruction module is designed to interchange information across modalities in the training phase by reconstructing masked images and reports. For reconstructing long reports, a sentence-wise prototype memory bank is constructed, enabling the network to focus on low-level localized visual and high-level clinical linguistic features. Additionally, a non-auto-regressive generation paradigm is proposed for reconstructing non-sequential reports. Experimental results on five downstream tasks, including supervised classification, zero-shot classification, image-to-text retrieval, semantic segmentation, and object detection, show the proposed method outperforms other state-of-the-art methods across multiple datasets and under different dataset size settings. The code is available at https://github.com/QtacierP/PRIOR.

Overview of "PRIOR: Prototype Representation Joint Learning from Medical Images and Reports"

The paper "PRIOR: Prototype Representation Joint Learning from Medical Images and Reports" presents a novel framework for vision-language pre-training, particularly applied to medical imaging. The motivation stems from the challenges inherent to medical image analysis: limited labeled data, domain gaps, and the need for fine-grained feature extraction. The authors propose PRIOR, which integrates prototype representation learning with both global and local alignment strategies to improve cross-modality interactions between medical images and textual reports.

Key Contributions

PRIOR introduces several innovative components that are crucial for enhancing the representation learning from medical images and reports:

1. Cross-Modality Alignment Module: This module is designed to capture fine-grained features by aligning global and local information. It helps in addressing the limitation of solely relying on global representations, which often overlook detailed local features necessary for medical image analysis.
2. Sentence Prototype Memory Bank (SPB): A key innovation is the SPB, which converts continuous sentence embeddings into discrete prototype representations. This allows for a classification-like task at the sentence level, improving the ability to focus on clinically relevant high-level representations.
3. Cross-Modality Conditional Reconstruction (CCR): The CCR facilitates interaction between modalities by reconstructing masked sections of data. It uses a lightweight encoder-decoder architecture to rebuild masked images and sentence embeddings, enhancing the model’s understanding of the structural and causal information present in medical images and reports.
4. State-of-the-Art Performance: The model demonstrates superior performance across five diverse medical imaging tasks: supervised classification, zero-shot classification, image-to-text retrieval, semantic segmentation, and object detection. These results highlight its potential for wide applicability in medical imaging applications.

Implications and Future Directions

The PRIOR framework represents a significant step forward in the integration of vision and language in medical imaging. Its ability to leverage detailed local and global representations across modalities allows for improved performance in complex tasks that depend on both high-level and low-level features. The model’s architecture is particularly suited for applications where detailed interpretability and fine-grained image understanding are essential, such as in diagnostic tasks requiring precise localization and characterization of medical conditions.

In terms of future directions, this work opens up several avenues for further exploration:

• Scalability and Generalization: Testing PRIOR on larger and more varied datasets could yield insights into its scalability and generalization capabilities. This includes its application to different medical modalities and multi-institutional datasets.
• Integration with Clinical Workflows: By integrating such models into clinical workflows, there may be opportunities to augment decision-making processes in real-time, potentially improving diagnostic accuracy and efficiency.
• Extensions to Other Domains: The architectural principles in PRIOR could be adapted for other domains where image-text relationships are crucial, such as autonomous driving systems or satellite imagery analysis.

In conclusion, PRIOR represents a sophisticated approach to enhancing the synergy between visual and linguistic data in medical imaging, providing a robust framework expected to impact both research and clinical applications significantly. Its emphasis on prototype learning and local-global alignment offers a nuanced methodology well-suited to the demands of real-world medical image analysis.