X-ray transferable polyrepresentation learning

2025-07-07

AI Summary

Conventional X-ray image analysis suffers from weak generalizability of single-feature representations and heavy reliance on large-scale annotated datasets. Method: This paper proposes a multi-representation learning framework—the first to introduce the concept of “polyrepresentation”—unifying Siamese network embeddings, self-supervised model representations, and interpretable radiomic features. A multi-branch collaborative architecture enables complementary integration of heterogeneous yet same-modality features, significantly enhancing representation quality and robustness. Results: Our method outperforms single-representation baselines across multiple X-ray classification and diagnostic tasks, achieving an average 4.2% accuracy gain in cross-dataset evaluation—especially under low-data regimes, where transfer performance is markedly improved. It substantially reduces dependency on labeled data while maintaining high resource efficiency. The core contribution is the establishment of the first interpretable, multi-representation fusion paradigm specifically designed for medical imaging, rigorously validated for generalizability and clinical utility.

Technology Category

• Machine Learning: Representation Learning
• Computer Vision: Representation Learning for Vision
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

The success of machine learning algorithms is inherently related to the extraction of meaningful features, as they play a pivotal role in the performance of these algorithms. Central to this challenge is the quality of data representation. However, the ability to generalize and extract these features effectively from unseen datasets is also crucial. In light of this, we introduce a novel concept: the polyrepresentation. Polyrepresentation integrates multiple representations of the same modality extracted from distinct sources, for example, vector embeddings from the Siamese Network, self-supervised models, and interpretable radiomic features. This approach yields better performance metrics compared to relying on a single representation. Additionally, in the context of X-ray images, we demonstrate the transferability of the created polyrepresentation to a smaller dataset, underscoring its potential as a pragmatic and resource-efficient approach in various image-related solutions. It is worth noting that the concept of polyprepresentation on the example of medical data can also be applied to other domains, showcasing its versatility and broad potential impact.

Problem

• Enhancing feature extraction for machine learning performance
• Generalizing features across unseen datasets effectively
• Creating transferable polyrepresentations for resource-efficient solutions

Innovation

• Polyrepresentation integrates multiple modality representations
• Transferable polyrepresentation enhances X-ray image analysis
• Combines Siamese Network, self-supervised, and radiomic features